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Le deuxième gouvernement Jean-Marc Ayrault est le trente-sixième gouvernement de la Cinquième République française. Après avoir présenté la démission de son gouvernement à la suite des élections législatives, le Premier ministre sortant Jean-Marc Ayrault est, en application de l' de la Constitution, reconduit dans ses fonctions par un décret du président de la République en date du .

Les autres membres du gouvernement sont nommés par un décret en date du . Il s'agit du deuxième gouvernement nommé par le président de la République François Hollande.

Le , en réponse à la défaite de la gauche aux élections municipales de 2014, Jean-Marc Ayrault présente la démission de son gouvernement, qui est acceptée le même jour.

Composition

À la suite des décrets des 18 et , du  et du , le gouvernement compte :
  femmes pour  hommes ;
  déléguées femmes pour  délégués hommes.

À sa nomination initiale, la parité au sein du gouvernement, promesse de campagne de François Hollande, a été respectée selon l'Observatoire de la parité entre les femmes et les hommes : si on exclut le Premier ministre, il y a en effet  et  parmi les ministres. Toutefois, à l'exception du ministère de la Justice, les principaux ministères régaliens sont occupés par des hommes.

Entre le  et le , c'est-à-dire entre la démission de Jérôme Cahuzac et le remplacement de Delphine Batho par Philippe Martin, le gouvernement compte parmi les ministres  et . Après la démission de Delphine Batho, il est composé de  et .

À la suite de l'affaire Cahuzac, les membres du gouvernement ont dû dévoiler leur patrimoine.

Composition initiale

Premier ministre

 Jean-Marc Ayrault, Premier ministre

Ministres

 Laurent Fabius, ministre des Affaires étrangères
 Vincent Peillon, ministre de l'Éducation nationale
 Christiane Taubira, garde des Sceaux, ministre de la Justice
 Pierre Moscovici, ministre de l'Économie et des Finances
 Marisol Touraine, ministre des Affaires sociales et de la Santé
 Cécile Duflot, ministre de l'Égalité des territoires et du Logement
 Manuel Valls, ministre de l'Intérieur
 Nicole Bricq, ministre du Commerce extérieur
 Arnaud Montebourg, ministre du Redressement productif
 Delphine Batho, ministre de l'Écologie, du Développement durable et de l'Énergie
 Michel Sapin, ministre du Travail, de l'Emploi, de la Formation professionnelle et du Dialogue social
 Jean-Yves Le Drian, ministre de la Défense
 Aurélie Filippetti, ministre de la Culture et de la Communication
 Geneviève Fioraso, ministre de l'Enseignement supérieur et de la Recherche
 Najat Vallaud-Belkacem, Ministre des Droits des femmes, porte-parole du gouvernement
 Stéphane Le Foll, ministre de l'Agriculture, de l'Agroalimentaire et de la Forêt
 Marylise Lebranchu, ministre de la Réforme de l'État, de la Décentralisation et de la Fonction publique
 Victorin Lurel, ministre des Outre-mer
 Sylvia Pinel, ministre de l'Artisanat, du Commerce et du Tourisme
 Valérie Fourneyron, ministre des Sports, de la Jeunesse, de l'Éducation populaire et de la Vie associative

Ministres délégués

 Jérôme Cahuzac, ministre délégué au Budget auprès du ministre de l'Économie et des Finances
 George Pau-Langevin, ministre déléguée à la Réussite éducative auprès du ministre de l'Éducation nationale
 Alain Vidalies, ministre délégué aux Relations avec le Parlement auprès du Premier ministre
 François Lamy, ministre délégué à la Ville auprès de la ministre de l'Égalité des territoires et du Logement
 Bernard Cazeneuve, ministre délégué aux Affaires européennes auprès du ministre des Affaires étrangères
 Michèle Delaunay, ministre déléguée aux Personnes âgées et à l'Autonomie auprès de la ministre des Affaires sociales et de la Santé
 Benoît Hamon, ministre délégué à l'Économie sociale et solidaire et à la Consommation auprès du ministre de l'Économie et des Finances
 Dominique Bertinotti, ministre déléguée à la Famille auprès de la ministre des Affaires sociales et de la Santé
 Marie-Arlette Carlotti, ministre déléguée aux Personnes handicapées et à la Lutte contre l'exclusion auprès de la ministre des Affaires sociales et de la Santé
 Pascal Canfin, ministre délégué au Développement auprès du ministre des Affaires étrangères
 Thierry Repentin, ministre délégué à la Formation professionnelle et à l'Apprentissage auprès du ministre du Travail, de l'Emploi, de la Formation professionnelle et du Dialogue social
 Yamina Benguigui, ministre déléguée à la Francophonie auprès du ministre des Affaires étrangères
 Frédéric Cuvillier, ministre délégué aux Transports, à la Mer et la Pêche auprès de la ministre de l'Écologie, du Développement durable et de l'Énergie
 Fleur Pellerin, ministre déléguée aux Petites et Moyennes entreprises, à l'Innovation et à l'Économie numérique auprès du ministre du Redressement productif
 Kader Arif, ministre délégué aux Anciens combattants auprès du ministre de la Défense
 Anne-Marie Escoffier, ministre déléguée à la Décentralisation auprès de la ministre de la Réforme de l'État, de la Décentralisation et de la Fonction publique
 Guillaume Garot, ministré délégué à l'Agroalimentaire auprès du ministre de l'Agriculture, de l'Agroalimentaire et de la Forêt
 Hélène Conway-Mouret, ministre déléguée aux Français de l'étranger auprès du ministre des Affaires étrangères

Remaniement du 19 mars 2013

Jérôme Cahuzac, ministre délégué au Budget, démissionne à la suite de l'ouverture d'une information judiciaire. Il est remplacé à ce poste par Bernard Cazeneuve. Ce dernier, jusqu'ici ministre délégué aux Affaires européennes, est lui-même remplacé par Thierry Repentin, alors ministre délégué à la Formation professionnelle. Ce dernier départ, qui n'est pas comblé par l'arrivée d'un nouveau ministre, rompt la parité au sein du gouvernement.

Ajustement du 2 juillet 2013
Le , François Hollande met fin aux fonctions de Delphine Batho, ministre de l'Écologie, du Développement durable et de l'Énergie, après qu'elle a donné une interview dans laquelle elle contestait les choix budgétaires du gouvernement. Elle est remplacée par Philippe Martin.

Galerie

Premier ministre

Ministres

Ministres délégués

Composition politique

Le gouvernement Jean-Marc Ayrault II est composé principalement de personnalités venant du PS :
 parmi les ministres :
 17 appartiennent au Parti socialiste ;
 2 appartiennent au Parti radical de gauche ou en sont proches ;
 1 appartient à Europe Écologie Les Verts.
 parmi les ministres délégués :
 14 appartiennent au Parti socialiste ;
 1 appartient au Parti radical de gauche ;
 1 appartient à Europe Écologie Les Verts ;
 1 est divers gauche.

À l'inverse des  qui précèdent, le gouvernement de Jean-Marc Ayrault est constitué sans « ouverture » vers des personnalités issues de l'opposition.

Provenance politique des ministres

Parmi les ministres :
 25 sont issus de l'Assemblée nationale (Jean-Marc Ayrault, Laurent Fabius, Pierre Moscovici, Marisol Touraine, Cécile Duflot, Manuel Valls, Philippe Martin, Aurélie Filipetti, Geneviève Fioraso, Stéphane Le Foll, Marylise Lebranchu, Victorin Lurel, Sylvia Pinel, Valérie Fourneyron, George Pau-Langevin, Alain Vidalies, François Lamy, Bernard Cazeneuve, Michèle Delaunay, Benoit Hamon, Marie-Arlette Carlotti, Frédéric Cuvillier, Kader Arif, Guillaume Garot, Michel Sapin, celui-ci étant alors député titulaire et devenant député suppléant).
 5 ne sont pas députés avant les élections législatives mais sont lors de celles-ci (Marie-Arlette Carlotti, Cécile Duflot, Stéphane Le Foll, Benoit Hamon, Kader Arif).
 4 détiennent un portefeuille ministériel dans un gouvernement précédent, celui de Lionel Jospin (Laurent Fabius, Marylise Lebranchu, Pierre Moscovici, Michel Sapin).
 4 sont issus du Sénat (Nicole Bricq, Thierry Repentin, Anne-Marie Escoffier, Hélène Conway-Mouret).
 2 sont issus du Parlement européen (Vincent Peillon et Pascal Canfin).
 7 sont issus de mandats locaux (Christiane Taubira, Arnaud Montebourg, Michel Sapin, Jean-Yves Le Drian, Najat Vallaud-Belkacem, Dominique Bertinotti, Yamina Benguigui).
 1 ne détenait aucun mandat politique avant d'être nommée (Fleur Pellerin).
 2 étaient députés lors du premier gouvernement de Jean-Marc Ayrault mais ne se sont pas représentés aux élections législatives de juin (Christiane Taubira, Arnaud Montebourg).
 1 était député de plein exercice et est devenu député suppléant : Michel Sapin (suppléant de Jean-Paul Chanteguet).
 2 étaient présidents de région (Jean-Yves Le Drian et Victorin Lurel).
 2 étaient présidents de département (Marisol Touraine et Arnaud Montebourg).
 9 étaient maires (Jean-Marc Ayrault, Manuel Valls, Michel Sapin, Valérie Fourneyron, François Lamy, Bernard Cazeneuve, Dominique Bertinotti, Frédéric Cuvillier et Guillaume Garot).
 4 étaient adjoints au maire (Laurent Fabius, Geneviève Fioraso, Najat Vallaud-Belkacem, Thierry Repentin).
 9 étaient présidents d'EPCI (Jean-Marc Ayrault, Laurent Fabius, Pierre Moscovici, Manuel Valls, Michel Sapin, François Lamy, Bernard Cazeneuve, Frédéric Cuvillier et Guillaume Garot).
 5 étaient vice-présidents d'EPCI (Geneviève Fioraso, Stéphane Le Foll, Valérie Fourneyron, Thierry Repentin, Kader Arif).

Composition par âge

Au moment de sa nomination, la moyenne d'âge du gouvernement Jean-Marc Ayrault II est de 53 ans :
 la ministre la plus jeune est Najat Vallaud-Belkacem, 34 ans, benjamine à quelques jours près de Sylvia Pinel ;
 la ministre la plus âgée est Anne-Marie Escoffier, 69 ans.

À cette même date, la répartition par tranche d'âge est la suivante :
 7 ministres ou ministres délégués ont moins de 40 ans.
 7 ministres ou ministres délégués ont entre 40 et 50 ans.
 11 ministres ou ministres délégués ont entre 50 et 60 ans.
 13 ministres ou ministres délégués ont plus de 60 ans.

Comparaison avec le précédent gouvernement

Portefeuilles divisés :
 le ministère de l'Économie, des Finances et du Commerce extérieur (Pierre Moscovici) est scindé en deux : un ministère de l'Économie et des Finances (Pierre Moscovici) et un ministère du Commerce extérieur (Nicole Bricq, précédemment ministre de l'Écologie, du Développement durable et de l'Énergie) ;
 la délégation de ministère aux Français de l'étranger et à la Francophonie (Yamina Benguigui) est scindée en deux : une délégation de ministère à la Francophonie (Yamina Benguigui) et une aux Français de l'étranger (Hélène Conway-Mouret).

Portefeuilles enrichis ou reformulés :
 le ministère de l'Agriculture et de l'Agroalimentaire est désormais ministre de l'Agriculture, de l'Agroalimentaire et de la Forêt ;
 le ministre délégué à l'Économie sociale et solidaire est désormais aussi chargé de la Consommation ;
 la ministre déléguée aux Personnes handicapées est désormais aussi chargée de la Lutte contre l'exclusion ;
 la délégation de ministère aux Transports et à l'Économie maritime s'appelle désormais délégation de ministère aux Transports, à la Mer et la Pêche ;
 la délégation de ministère aux Personnes âgées et à la Dépendance s'appelle maintenant délégation de ministère aux Personnes âgées et à l'Autonomie.

Changement de statut :
 la délégation de ministère à l'Artisanat, au Commerce et au Tourisme, précédemment rattachée au ministère du Redressement productif, est désormais un ministère à part entière

Délégations de ministères créées :
 la Décentralisation ;
 l'Agroalimentaire

Délégation de ministère supprimée :
 il n'y a plus de ministre déléguée auprès de la ministre de la Justice, garde des Sceaux.

Changement de ministre, à portefeuille identique :
 le ministère de l'Écologie, du Développement durable et de l'Énergie, précédemment confié à Nicole Bricq, est désormais à la charge de Delphine Batho

En termes d'effectifs, avec deux portefeuilles de ministres scindés, trois délégations créées et une supprimée, cela fait quatre ministres ou ministres délégués supplémentaires par rapport au premier gouvernement de Jean-Marc Ayrault.

Déclaration de politique générale
Le , le Premier ministre obtient la confiance de l'Assemblée nationale par 302 voix pour, 225 contre et 17 abstentions.

Motion de censure
Le , la motion de censure déposée par le groupe de l'Union pour la majorité présidentielle est rejetée avec  pour contre 287 requises.

Mesures prises par le gouvernement

Agenda économique
 Augmentation du SMIC de 2 %
 Augmentation de l'allocation de rentrée scolaire de 25 %
 Hausse du revenu de solidarité active (RSA) de 10 % en 5 ans
 Blocage des prix de l'essence sur les 3 premiers mois du quinquennat
 Encadrement des loyers
 Extension des tarifs sociaux pour l'eau, le gaz et l'électricité
 Relèvement du plafond du livret A de 25 % à  et doublement du plafond du livret de développement durable (LDD) à ); baisse du taux de rémunération du livret A qui passe de 1,5 à 1,25 %
 Création des contrats de génération : l'embauche d'un sénior de plus de 55 ans en CDI et d'un jeune de moins de 26 ans en CDI sera compensé par une aide financière en faveur des entreprises
 Loi sur la création de  emplois d'avenir entre 2012 et 2014 pour les jeunes de 16 à 25 ans peu ou pas qualifiés essentiellement dans les associations, les collectivités territoriales et l'économie sociale et solidaire vivant dans des zones urbaines ou rurales défavorisées, ou en outremer, ainsi qu'à des personnes handicapés peu qualifiés de moins de 30 ans, en difficulté. Ces emplois d'avenir sont majoritairement subventionnés par l'État et reprennent en grande partie l'idée des emplois jeunes du gouvernement Jospin.
 Organisation d'une grande conférence économique et sociale à l'été 2012 sur les priorités du quinquennat.

Agenda fiscal
 Hausse de l'impôt sur la fortune
 Augmentation de la tranche supérieure de l'impôt sur le revenu (IR) de 41 à 45 %
 Taxation supplémentaire des logements vacants
 Alourdissement des droits de succession
 Création d'une taxe à 75 % pour les sociétés qui versent des rémunérations supérieures à 1 million d'euros
 Augmentation du taux de droit commun de la TVA de 19,6 à 20 %
 Augmentation du taux intermédiaire de 7% à 10 %
 Création d'un crédit d'impôt pour la compétitivité et l'emploi (CICE)
 Annulation de la défiscalisation des heures supplémentaires pour toutes les entreprises, et restriction de l'exonération des cotisations sociales patronales aux entreprises de moins de vingt salariés seulement.
 Enveloppe d'aide de 50 milliards d'euros pour les entreprises afin de leur permettre d'embaucher
 Taxe supplémentaire de 3 % sur les dividendes, contribution additionnelle à l'impôt sur les sociétés.

Fin , Jean-Marc Ayrault annonce le lancement de négociations en vue d'une refonte de la fiscalité, afin d'établir des .

Agenda environnemental
 Réduction de la part du nucléaire dans la production de l'énergie en France (prévu à 75% d'ci 2025)
 Maintien du rejet de tous les permis d'exploration et d'exploitation du gaz de schiste par la technique de fracturation hydraulique, et ce durant tout le quinquennat.

Agenda éducation et recherche
 Recrutement de  postes supplémentaires sur cinq ans à l'Éducation nationale
 Rétablissement de l'année de stage dans la formation des nouveaux professeurs
 Mise en place de cours de morale de l'école primaire au lycée
 Réforme des rythmes scolaires: baisse des heures de classe la journée en échange d'une diminution des vacances scolaires
 Annulation de la prise en charge intégrale des frais de scolarité (PEC) pour les Français de l'étranger.
 Limitation du nombre de redoublements
 Loi sur l'adaptation et la sécurisation des parcours professionnels
 ABCD de l'Égalité (finalement abandonné à la suite de nombreuses et fortes protestations)

Agenda santé
 Suppression du droit d'entrée de  dans le dispositif de l'aide médicale d'État (AME).
 Plafonnement des dépassements d'honoraires dans le domaine de la santé (Avenant n°8 à la convention médicale)

Agenda social

 Abaissement de l'âge légal de départ à la retraite de 62 à 60 ans pour ceux qui ont commencé à travailler tôt
 Construction de logements sociaux
 Loi sur la Mobilisation du foncier public en faveur du logement et renforcement de la loi SRU: augmentation du taux de logements sociaux obligatoire à 25 % dans les communes de plus de  (et  en Île-de-France), 10 % dans les communes entre  et  faisant partie d'une agglomération de , multiplication par 5 des pénalités pour les communes qui ne respecteront pas cette obligation, réduction à 12 mois contre 18 auparavant du délai au terme duquel un logement est déclaré vacant et donc susceptible d'être réquisitionné et suppression de la possibilité de se soustraire à une réquisition en présentant un devis de travaux d'aménagement du logement.
 Annulation ou remise en cause de plusieurs grands projets d'infrastructures : liaison Seine-Escaut, autoroutes (Autoroute A355), lignes de TGV, Grand Paris Express.
 Loi du  sur le harcèlement sexuel comblant ainsi un vide juridique depuis que le conseil constitutionnel avait censuré le  un article de la loi sur le harcèlement sexuel, le jugeant contraire à la constitution.
 Loi du 24 mars 2014 pour l'accès au logement et un urbanisme rénové
 Proposition de nationaliser ou racheter les entreprises susceptibles d'être abandonnées ou fermées (ex. : cas d'Arcelor Mittal à Florange en 2013. Finalement le projet de nationalisation temporaire évoqué par le ministre du Redressement productif pour sauver les emplois est rejeté par le Premier ministre en ).

Agenda famille
 Légalisation du mariage et de l'adoption pour les couples de même sexe (loi no 2013-404 du )
 Création de  nouvelles places de crèche
 Diminution du plafond du quotient familial pour le calcul de l'impôt sur le revenu (IR)
 Suppression des réductions d'impôts pour frais de scolarité au collège et au lycée
 Augmentation de 25 % de l'allocation de soutien familial (ASF)
 Augmentation de 50 % du complément familial en faveur des familles nombreuses vivant sous le seuil de pauvreté

Consommation
 Loi Hamon renforçant les droits des consommateurs

Sécurité

(Loi  du  relative à l'individualisation des peines et renforçant l'efficacité des sanctions pénales)
 Suppression des peines planchers.
 Création d'une peine de probation pour les délits passibles de cinq ans ou moins.
 L'aménagement de peine automatique est réduit de 2 ans à 1 an pour le premier délits et de 2 ans à 6 mois pour les multi-récidivistes.
 Construction de 6500 places de prisons (au lieu des  prévues par l'ancienne majorité de droite).

Notes et références

Voir aussi

Articles connexes

 Élection présidentielle française de 2012
 Résultats des ministres lors des élections législatives françaises de 2012
 Élections législatives françaises de 2012
 Gouvernement Jean-Marc Ayrault I

Lien externe

 https://www2.assemblee-nationale.fr/decouvrir-l-assemblee/histoire/la-ve-republique/tous-les-gouvernements-depuis-1958/presidence-de-francois-hollande-2012-2017/gouvernement-ayrault-ii-21-juin-2012-31-mars-2014

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2012 en France
2013 en France
2014 en France


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text:
2 Mar 17
Mix Of The Week: Monki
Andrew Kemp | Features & Interviews
There's always something coming out the speakers in the Ticket Arena office, so we thought we'd share some of the sonic splendours that soundtrack all of our hard work. Now a weekly feature that brings a different artist to the fore each Thursday, the TA Mix Of The Week scours the vaults for some of our favourite sets of old and keeps a beady eye out for the classics of the future, serving up a taste of the artists that make us happy to do what we do.
2017 started with a new weekly show on BBC Radio 1 for Monki, and she's looking pretty keen to build on the good start as she moves through the year. A night at Liverpool's 24 Kitchen Street lies ahead on Thursday 9th March, with guests joining to take part in her latest tour, 'The Warning'. Part of a wider tour that will see her visit London, Leeds, Nottingham and Birmingham before finishing up at Southampton's Junk, the Liverpool leg will be a good chance for the BBC selector to show off her eclectic tastes and inspired selections.
This mix, recorded for Mixmag at the start of February, shows Monki working her way through chunky synth lines and punchy rhythms from the likes of Dalfie, Skream, Armand Van Helden and La-4a with the skillful execution and plenty of energy. A good introduction to the fun-first approach that the BBC DJ takes to her music, this one should get you right in the mood to see her at a festival this summer, with Annie Mac's Lost & Found and Parklife both having secured Monki's services behind the decks nice and early.
Photo courtesy of Monki
MonkiMix Of The WeekHouseGarageUK BassBBC R1

redpajama_set_name: RedPajamaCommonCrawl


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From the Critical Reading blog
How Hillary Clinton Promotes Islamophobia
Arun Kundnani on Hillary Clinton's record of imperialist intervention and how this strengthens discrimination against Muslims. --PG
Follow Critical Reading on Twitter: @CriticalReading. Subscribe to Critical Reading's public updates on Facebook.
Why Hillary's Neoconservative Foreign Policy Will Make The Problem of Islamophobia Worse
Clinton's neoconservative backers understand what her liberal supporters do not.
By Arun Kundnani / AlterNet August 7, 2016
In Khizr and Ghazala Khan, the Democratic Party seems to have found the perfect counter to Donald Trump. Since Trump proposed banning Muslims from the US, his campaign has sought to exploit the fear that Muslims are dangerous and disloyal. But who could think that of the patriotic, constitution-waving Khans, whose son died fighting for the U.S.?
Trump suggested that Ghazala Khan did not speak for Islamic reasons. But this backfired and the episode appears to have hurt him in the polls. Meanwhile Hillary Clinton has been able to establish herself as the candidate of tolerance and liberal progress.
But take a closer look and things are not that straightforward. It is easy to lose sight of why the Khans lost their son in the first place. Humayun Khan died fighting in the illegal war in Iraq, which was launched on the basis of Islamophobic lies, and supported by Hillary Clinton, as senator for New York.
In 2011, Clinton was a leading figure pushing for military action in Libya. She initially presented the bombing campaign as a way to create a no-fly zone to protect civilians. Within three weeks, the real aim became apparent: regime change. When news broke that former leader Muammar Qaddafi had been captured, beaten and killed, Clinton joked with a CBS News reporter: "We came, we saw, he died." Like in Iraq, the result was the complete unraveling of the country's social fabric, bringing insecurity and displacement to the mass of the population, while ISIS was able to exploit the chaos to establish a presence.
It seems clear that Clinton will continue her war-mongering foreign policy if she is elected. She will almost certainly increase military action in Syria. Around 600 civilians have already been killed by coalition bombing over the last two years, including 163 children. With Clinton in the White House, many more civilians will be killed by the U.S. military in the Middle East, while the root causes of conflict will go unaddressed. Policy will be more aligned with the Netanyahu right-wing in Israel than it has been over the last eight years. Clinton personally intervened to ensure no criticisms of Israel were heard at the Democratic Party convention.
The neoconservative Robert Kagan said of Clinton in 2014: "I feel comfortable with her on foreign policy.…If she pursues a policy which we think she will pursue, it's something that might have been called neocon, but clearly her supporters are not going to call it that; they are going to call it something else." In other words, she will pursue a neoconservative foreign policy dressed up in the liberal vocabulary of humanitarian intervention. Little wonder that neoconservatives like Kagan have turned to support her instead of Trump, and as actively as possible -- Kagan recently hosted a fundraiser for Clinton in Washington DC.
Her neoconservative backers understand what her liberal supporters do not: when a conflict arises between the liberal values she espouses and the demands of U.S. empire, she always chooses power over principle. When genuine democratic movements emerge that threaten U.S. interests, such as in Honduras in 2009 and in Egypt in 2011, Clinton sides with her authoritarian friends rather than with the revolutionaries on the streets.
That has implications within the U.S. too. So long as the government keeps going to war in the Middle East, it will be hard to counter anti-Muslim prejudices among ordinary Americans. Around half of Americans believe Islam is more likely than other religions to encourage violence among its believers. That belief is false: in fact, global polls indicate that Americans are more supportive of violence against civilians than people in other parts of the world. Attitudes toward violence have little to do with religious belief.
But the belief in Islam's inherent violence thrives because it provides a convenient and simplistic explanation for the violent conflicts in the Middle East, without having to acknowledge the U.S. government's own role in fostering and exacerbating that violence. Trump's Islamophobia is a symptom of this deeper problem. But Clinton's neoconservative foreign policy will make the problem worse.
If the United States follows her lead, acceptance of Muslims may be dependent on whether they seem as patriotic as the Khans. Those who dissent or criticize the U.S. government or Israel will continue to be treated as suspicious and disloyal. Viewed from this angle, Clinton's embrace of patriotic Muslims like the Khans seems less about liberal principles and more about creating a PR image to deflect criticisms of U.S. foreign policy. That's not treating Muslims as fellow citizens; it's treating them as pawns on a geopolitical chessboard. Such positive images of Muslims may help stop Trump but they can also be used to strengthen the structures of anti-Muslim racism in domestic and foreign policy.
So go ahead and support Clinton to keep out Trump. But don't feel too smug about being a good liberal if you do. Really, what you're doing is choosing Clinton's bombs over Trump's bans.
Todd Chretien
Socialist organization in the time of Trump
Thousands of people are asking whether it's time to join the socialists in order to resist Trump--and the system that gave rise to him in the first place.
The left-out and left-behind economy
The plague of unemployment that took hold during the Great Recession is still upending the lives of millions of U.S. workers--with no end in sight.
Anti-imperialism and the Syrian Revolution
The Arab Spring uprising in Syria has tested the left by posing a blunt question: Are you on the side of a dictator--or that of a popular uprising?


Call it a day. – It Is Better Outside.
After riding for a couple of days non stop, we decided to call it a day. And hands down, it was a awesome day to skip the chilly Brecken-fridge. That morning was way too cold to be outside. We started the car, and a couple minute later after de-freezing we head to Boulder.
Boulder its a beautiful city located at the foothills of the Rocky Mountains of Colorado, NW of Denver. It is famous for it Western history, being a choice of destination of the late 60's hippies. We stop by many interesting towns across the way.
Wow! Amazing photos. It's as if I'm there!
thanks! can you feel the cold?!
that's rad! you'll get used to the snow a lo there… banff/ canada is amazing!
very cool guys, hope you found a warm cup of coffee!


Federalist had been"d 291 times in Supreme Court decisions. Consequently, the great problem in framing a government is that the government must be able to control the people, but equally important, must be forced to control itself. To assert after reading this passage that. Hoboken, NJ: John Wiley Sons. 37 through 58 by Madison, written while Hamilton was in Albany, and. Philosophy, The Federalist, and the Constitution, New York: 1987. Retrieved The Federalist: a Collection of Essays, Written in Favour of the New Constitution, as Agreed upon by the Federal Convention, September 17, 1787, in two volumes (1.).
Get Your essay Now! Madison, who is now acknowledged as the father of the Constitutiondespite his repeated rejection of this honor during his lifetime, 12 became a leading member of the.S. New York held out until July 26; certainly The Federalist was more important there than anywhere else, but Furtwangler argues that it "could hardly rival other major forces in the ratification contests"specifically, these forces included the personal influence of well-known Federalists, for instance Hamilton and. Bull, apparently the first decision to mention The Federalist ). Isbn.,.51 a b c d Nos. Hoboken, NJ: John Wiley Sons Wills, xii. Patrick, John., and Clair. Who didn't support the ratification of the Constitution? If you include anything irrelevant in your paper, readers will struggle to follow the main thread of your thoughts and lose their focus even on good points. What you need to do next. Why practice makes perfect, one of the most effective ways to answer assignment questions correctly and fully is to write a plan before the entire piece.
Get basic information about them if teachers ask you to complete any written assignment on this subject. This document was a target of criticism from many opponents after the US Constitution was sent to the Confederation Congress, and it happened in 1787. It will become obvious to readers at once, so read assignment questions a few times to understand everything clearly. Madison commenced the statement of his theory in Federalist 51 with an acknowledgement that the "have nots" in any society are extremely likely to attack the "haves." Like Hamilton, the Virginian believed class struggle to be inseparable from politics.
Many questions are formulated in ways that leave room for only one acceptable and safe answer. Essay is a reflection of the writers ideas. All the ideas and views should strictly relate to..
We experience properties appearing in regular clumps, but we must infer that there is something that supports or perhaps holds together those qualities. Of enthusiasts, those who would abandon reason and claim to..
Master thesis dissertation quiz farmers alliance essay corporate social responsibility research papers journals? Samford university application essay where can i buy an essay online order, dissertation calculator. Leisure is enjoyable only after fruitful..


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Tagged WHO
70th Anniversary of the Universal Declaration of Human Rights
ASH in the News, Blog, Featured News & Events, News & Events
December 10 is international Human Rights Day, in observation of the day that the UN General Assembly adopted the Universal Declaration of Human Rights (UDHR). This landmark document set out, for the first ...
Statement from ASH Executive Director Laurent Huber on new PMI Foundation
ASH in the News, Featured News & Events, News & Events, Press Releases
On September 13, a new chapter in the struggle against tobacco began with a surprise announcement. Philip Morris International, a multinational giant and marketer of Marlboro, the world's most popular cigarette...
The U.S. Is Not Living Up to Its Obligation to Protect the Health of Its Citizens
All countries around the world, including the United States, have committed to the 2030 Agenda for Sustainable Development: the Sustainable Development Goals (SDGs), which aim to strengthen universal peace and ...
The world's largest tobacco company wanted good press – but it backfired
Re-printed as an English translation with permission from Buzzfeed Germany, original article in German here. Stop producing or marketing tobacco! This is what an Institute for Human Rights has recommended to ...
We Agree: Put Big Tobacco out of business
The embattled tobacco industry is struggling to fight off one of its fiercest and possibly most dangerous foes to date: the World Health Organization. The Hong Kong native who has run the U.N. body for the p...
New research published by the World Health Organization (WHO)
This new research by the WHO shows the huge opportunity governments have from increasing taxes on tobacco. WHO found that if cigarette tax increased by US$ 0.8 (or 1 international $) per pack wor...
Does Tobacco Violate Human Rights?
FOR IMMEDIATE RELEASE Contact: Megan Arendt Office: 202-659-4310 Email: arendtm@ash.org Does Tobacco Violate Human Rights? Inter-American Commission Ponders Question WASHINGTON, D.C. – April 6, 2016...
WHO warns governments that Big Tobacco is undermining protocol to combat smuggling
The Secretariat for the WHO Framework Convention on Tobacco Control (FCTC) recently sent the following Note Verbal to all Missions of Parties in Geneva to alert them to prevent interference from the tobacco ind...
New Global Anti-tobacco Leadership
FOR IMMEDIATE RELEASE Contact: Megan Arendt Email: arendtm@ash.org New Global Anti-tobacco Leadership After twelve years of success, ASH passes FCA baton to HealthBridge WASHINGTON, D.C. – January 20,...
2015 ASH Newspaper
Blog, E-Newsletter, Featured News & Events
Huge progress made thanks to court case against Dutch State
Dutch government draws up rules for contacts with tobacco industry via Youth Smoking Prevention Foundation Amsterdam, 9 November 2015 – The court in The Hague ruled against the Youth Smoking Prevention Founda...
Released: WHO Report on the Global Tobacco Epidemic, 2015
The WHO Report on the Global Tobacco Epidemic, 2015 has been released. It is available here>...
#USChamberExposed
ASH in the News, Featured News & Events, News & Events, Related News
On June 30, 2015, The New York Times released a tiered investigation to expose the U.S. Chamber of Commerce and the American Chamber of Commerce (AmCham) affiliates as a front group for Big Tobacco. The New ...
International Smoke-free Air Successes
Many Americans view smoking, and secondhand smoke, as a problem that has mostly been solved, at least in the United States. However, only half of all Americans are protected from exposure to secondhand smoke...
#StopTobaccoTactics
Blog, Featured News & Events, News & Events
Earlier this year Tel Aviv University's School of Marketing, under pressure from the Israel Cancer Association, canceled an event sponsored by Philip Morris International. Unfortunately, victories like this...
"We Have an Opportunity to Change History"
"Health must become a priority. The time is now," said global tobacco control advocates in the Framework Convention Alliance video. They emphasize the importance of this year, 2015, and that leaders must ra...
Why charities should not let Big Tobacco use them for marketing. Earlier this month, a journalist caused a stir among health charities by publicizing the fact that the American Red Cross accepts donations fro...
Legal Victories in International Tobacco Control 2014
Blog, Smoke Alarms, Smoke Alarms: ASH Legal Updates
Philip Morris International was fined in Brazil for targeting youth The consumer protection agency from the Brazilian state of São Paulo has fined Philip Morris over $480,000. The agency acted after a f...
Malaysia Defends Tobacco Control in TPP & FCTC
Almost 200 countries signed the World Health Organisation's Tobacco Control Convention and are obliged to take measures to curb tobacco use. But the industry has hit back. A big tobacco company, Philip Morris,...
Stubbing Out the Tobacco Industry's Abuse of Trade Agreements
The tobacco industry has a long history of flexing its muscles, namely in the area of investor protection schemes, against governments in the name of protecting its own market. TTIP is an opportunity to set a g...
In Putin snub, US will skip global tobacco summit hosted by Russia
In a shot at Russian President Vladimir Putin, the United States will not send a delegation to Moscow this month to participate in global health talks that hold major implications for the tobacco and burgeoning...
A Winnable Battle
Blog, Smoke Alarms
The CDC identifies reducing tobacco as a "Winnable Battle" because tobacco is a public health priority with "large-scale impact on health and with known, effective strategies to address them." For ASH, the a...
'Ban E-cigarette use indoors,' says WHO
The World Health Organization says there should be a ban on the use of e-cigarettes indoors and that sales to children should stop. In a report the health body says there must be no more claims that the device...
Why Uruguay's David and Goliath fight with big tobacco really matters
LIMA, Peru — A protracted legal battle in an obscure World Bank tribunal over the principles of market competition in a South American backwater. Even by trade dispute standards, this one sounds arcane — the pe...
World Health Organisation says Pacific considering cigarette plain packaging
Featured News & Events, Related News
The World Health Organisation says a number of countries across the Pacific are considering following in Australia's footsteps and introducing plain packaging of cigarettes. The WHO is set to join governments ...


text:
N.Y.U. Putting $1 Billion Into Its Engineering School in Brooklyn
The investment bore fruit: In 2006, the school was at number 82 in the U.S. News and World Report rankings. This year, it ranked 33.
The investment in the school comes as N.Y.U.'s president, Andrew Hamilton, is planning to retire next year. "I came to N.Y.U. with several priorities, among them improving affordability, increasing diversity, enhancing the sciences, and building upon our foundation in Brooklyn," Mr. Hamilton, a former chemistry professor at Yale, said in a statement.
"Engineering education is a force for social mobility, an economic engine for the borough, and a vital contributor to the city's effort to be a world center of tech," he added.
Still, an education at Tandon may be out of reach for many New Yorkers: The total sticker price to attend for on-campus N.Y.U. students, which includes Tandon, is currently more than $83,000 a year.
Lower-income students typically pay less because of financial aid — about $23,000 in the 2020-21 school year, according to federal data — but they can still expect to pay significantly more for an education at N.Y.U. than at many other higher-education institutions in the city, including public colleges and some private universities like Columbia.
The sticker price at Columbia for students to attend and live on campus is more than it is at N.Y.U., for example, but the lowest-income students at Columbia paid on average less than $10,000 a year in the 2019-20 school year after financial aid was factored in, according to the latest year of data available on the federal site, College Navigator. At other private universities in the region, such as Fordham University and The New School, lower-income students pay similar amounts on average to what they would pay at N.Y.U.
At CUNY's College of Technology, the overall price for in-state students was close to $26,000, but, after aid, dropped to less than $2,500 for the lowest-income students.
News Room1 min ago
Pro-Trans Protesters Disrupt Rally Against Child Sex Changes
Demonstrators gathered in Murfreesboro, Tennessee, to rally against the transitioning of children and were met with counter-protesters who attempted to disrupt the event. Participants...


How to Use Twitter Moments for Business by Ana Gotter on Social Media Examiner.
The result is a slideshow of curated content that tells a story in a dynamic way.
Use Twitter Moments to tell stories in new and interesting ways.
If you go to the Moments tab, you'll find select Twitter moments, which are sorted by topics of interest. The most recent and relevant moments are displayed first.
Moments can be organized into different categories, making it easier for users to find content that interests them.
If you go to the Moments tab on your account page, you'll be able to see any moments you've created.
You'll find your Twitter moments on the Moments tab of your profile.
To create a Twitter moment, click your profile photo in the top-right corner and select Moments from the drop-down menu.
To access Twitter Moments, click your profile photo and select Moments from the drop-down menu.
Then click Create New Moment on the right.
Click Create New Moments on the Moments tab of your profile.
Next, choose a title for your moment and add a description that will help grab users' attention and generate engagement and conversation.
The title of your moment will be public and visible, so make sure it's compelling enough to interest users.
To add tweets to your moment, select the grey checkmark next to the individual tweets.
Next, add a cover image to the moment. This image is important, because it will help draw users' attention and get them to click to see more. You can upload an image, or use an image from a tweet you've already added to your moment.
You can upload an image from your computer or choose an image from the moments you've already selected.
Set a mobile theme color.
Decide whether to share your location with the moment.
Mark moments that have sensitive material.
Delete or unpublish moments (this can come in handy later).
You can adjust various Twitter moment settings, such as the mobile theme color or mark it as having sensitive material.
When you're ready to publish the moment, click Publish in the top-right corner.
Click Publish to publish your Twitter moment.
After you've published your moment, you can compose a tweet to promote it and share it on your feed.
Once you publish your moment, you'll be prompted to create a tweet to promote it.
Moments are easy to spot thanks to the distinctive lightning bolt symbol attached to them.
Twitter moments are best used to highlight a specific topic of your choice, and can be used to increase engagement and spark conversation. While only a few businesses are using Twitter Moments so far, there are some good examples that you can adapt for your own marketing efforts.
Partnering with influencers and celebrities can help you generate a flood of immediate traffic and skyrocket visibility on your campaigns. If an industry expert or other major influencer has interacted with your business, make sure you feature it in a moment.
When actress Octavia Spencer tweeted about her upcoming movie Gifted, it was featured as the first tweet in the moment about the movie's new trailer. It also included tweets that tagged stars in the movie like Chris Evans and Jenny Slate.
Featuring influencers in your moments can help you gain more momentum and visibility.
While most small and medium size businesses don't typically have celebrities endorsing their products, featuring tweets from industry experts is still a huge deal. Place the tweet from the influencer at the beginning of the moment to ensure that it's seen immediately.
Twitter moments are a great way to promote an event, whether it's upcoming, happening live, or has already passed.
Derby Central's Twitter moment promotes their upcoming tournament event. It features their own tweets to give users all of the pertinent information they need to know for the tournament, including previews, predictions, and upcoming matchups.
Twitter Moments is a great way to promote your events, getting users hyped about what's coming and giving them all the information they need in one place.
Featuring some of your own tweets in a moment allows you to distribute important information of value to those who may participate in the upcoming event. Using a mix of your informative messages along with tweets from other users can be a strong promotional tool for any event you're a part of.
You can also share Twitter moments during a live event. IBM Travel created a Twitter moment during the 2016 Aviation Festival, which focuses on customer service management for airlines and airports.
Share Twitter moments during a live event.
Video has made a huge impact on social media, and incorporating video tweets in your moment can be an effective way to tell a story. Using a video as your cover photo can also get your moment a ton of traction.
This moment from the NBA's Twitter account showcases a big play from an NBA basketball game. It includes videos of the same play shot from different angles, keeping users scrolling through the entire moment and maximizing interest and engagement. The videos are dynamic, and you can feel the excitement from the game and the audience watching it from the arena.
Using video in your Twitter moment can help boost engagement and improve results.
As an added bonus, the NBA's moment was timely, meaning it was highly interesting to users and there was a ton of content to choose from to add to the feed. This only strengthens the campaign and improves success.
Twitter moments are a great way to generate conversation. Because you're able to choose which tweets you want to include in moments, you can decide which interactions you want to highlight. Telling stories this way can positively impact your business and help you reach people you haven't connected with before.
What do you think? Have you used Twitter moments? How will you use them to promote your business? Share your thoughts, knowledge, and experience in the comments below!


If a vampire has votes, but no title, and gains a title does he add the votes of the title to the votes of himself?
For instance, my vampire's card text says he has 1 vote and is sabbath, if he would gain a archbishop title would he give me 3 votes each referedum?
Replied by Emin on topic untitled votes and titles?
i think that the title replace the "untitled".
nick wrote: If a vampire has votes, but no title, and gains a title does he add the votes of the title to the votes of himself?
Which vampire are you referring to?
Normally, crypt cards that have the text 'has 1 vote' or '1 vote' where you would normally read a title, are considered titled vampires (ie titled independent vampires).
it is a crypt card in this case danielle diron who's card text says she has 1 vote versus randall who's card text says he has 1 vote (titled) but of course randal is of a newer set. i'l refrase do votes coming from a crypth card that says "has x votes" always count as a title?
Replied by hardyrange on topic untitled votes and titles?
"Danielle has 1 vote (titled). "
Independent: Danielle has 1 vote (titled). During your untap phase, if Danielle is ready and has no blood, move 2 blood from the blood bank to her.


Doing the right thing…knowing the right thing to do. This is a question that we ask ourselves everyday as school leaders. Richard Elmore, professor of Educational Leadership at Harvard University, published a paper with this title through the NGA Center for Best Practices (can be found here). He offers suggestions and practical advice on how schools can get better. After reading Elmore's article graduate students in the Dordt College School Leadership program blogged similar advice, written specifically for Christian school leaders. Our fifth guest blogger is Jander Talen, Music teacher at Cochrane High School in Calgary, Alberta.
Close the Gap – Elmore explains early in his book that one of the major issues in schools is the large gap between what we know to be true/best practices and what we're actually doing as educators. Schools need to provide their teachers with the resources, education, and assistance to implement what we know to be true/effective in the classroom.
Clue into True Teaching – What we think to be good teaching and what actually is good teaching is skewed. When we walk by a classroom and see all students smiling with eyes on the teacher and an animated educator in the front, we immediately think, "That must be a good teacher!" Why do we think this? Students are engaged, teacher seems excited and enthused, and classroom management is a non-issue. But what are the students learning? What are the students doing? Elmore explains that often in these cases the teacher does all the work and ends up leaving the students with little or nothing to do themselves. Elmore writes about a neat evaluation question that I believe we should be using in our classrooms. He writes, "If you walk into a classroom and sit down next to a student, ask them what they're doing and why, and if you don't get a clear answer, it is highly unlikely that any powerful learning is taking place" (240). Our schools would be better if our students could answer these questions.
Collaborate – Wisdom from Within (SC vs. HC – Social vs. Human Capital) – While HC is important (abilities, knowledge, skills), Carrie Leana explains that SC (collaborative working environment/relationships), if implemented correctly, produces higher student output than HC. If you have 25 individuals with high levels of HC, the student output will generally be lower than if 25 individuals with low HC invested in SC. Student output then would be highest when individuals with high HC invest in SC. We need SC in our schools to improve schools – read Leana's article here.
Climbing Mountains Requires Some Respite – Don't undermine the improvement process by expecting all problems tackled to be solved quickly (Masterpieces take time – the Mona Lisa took around 10 years!). Look at it like climbing a high altitude mountain (Elmore's analogy p.248). Climbers will climb, then wait, and repeat multiple times before they make their final assault on the summit. These times of waiting are for acclimatizing, resting, reviewing progress, taking inventory of supplies, planning for the next increase in altitude, etc. We need to do the same when we're improving, when we hit a flat line, don't give up and think our progress has been in vain. Take time to reflect, re-adjust, and remember the route and why you're doing what you're doing.
Jander Talen is originally from PEI, Canada and a Dordt College graduate. Jander is currently living in Calgary, Alberta with his wife Tiffany and serving as a music teacher with the Rocky View School District (Cochrane High School). Previously, he and his wife lived in Uganda where he taught at Heritage International School.


text:
\section{Introduction}
Semantic dependency parsing (SDP) attempts to identify semantic relationships between words in a sentence by representing the sentence as a labeled directed acyclic graph (DAG), also known as the semantic dependency graph (SDG).
In an SDG, not only semantic predicates can have multiple
or zero arguments, but also words from the sentence
can be attached as arguments to more than
one head word (predicate), or they can be outside
the SDG (being neither a predicate nor an argument).
SDP originates from syntactic dependency parsing which aims to represent the syntactic structure of a sentence by means of a labeled tree.

In syntactic and semantic dependency parsing, higher-order parser generally outperforms first-order parser \cite{ji2019graph,wang2019second,zhang2020efficient}.
The basic first-order parser scores dependency edges independently, and the higher-order parser takes relationships between the head and modifier tokens and the $K$-order neighborhoods of them into consideration.
Here is a straightforward example (as in Figure \ref{fig:higher_order_eg}). For the sentence \emph{They read Mickey Spillane and talk about Groucho and Harpo}, its semantic dependency representation is shown in Figure \ref{gold annotation}, part of higher-order features appeared in this sentence are shown in Figure \ref{higher-order features}.
When considering the semantic dependency relationship between \emph{talk} and \emph{Harpo} (dotted edge), if we have known (1) \emph{Groucho} and \emph{Harpo} are included in the $2^{nd}$-order feature (\emph{sibling}) and (2) there is a dependency edge labeled \emph{PAT-arg} between \emph{talk} and \emph{Groucho} (blue edges), it is obvious to see that there is also a dependency edge labeled \emph{PAT-arg} between \emph{talk} and \emph{Harpo}.

\begin{figure}[!ht]
	\centering
	\vspace{-0.35cm}
	\subfigtopskip=2pt
	\subfigbottomskip=2pt
	\subfigcapskip=-2pt
	\subfigure[PSD formalism of the example sentence]{
		\label{gold annotation}
		\includegraphics[width=0.46\textwidth]{imgs/brat-gold-20037004.pdf}}
	\subfigure[Part of higher-order features]{
		\label{higher-order features}
		\includegraphics[width=0.46\textwidth]{imgs/higher-order_features-20037004.pdf}}
	\caption{Part of $2^{nd}$-order, $3^{rd}$-order, $4^{th}$-order features appeared in the example sentence.}
	\label{fig:higher_order_eg}
\end{figure}

Several semantic dependency parsers are presented in recent years.
Most of them are first-order parsers
\cite{du2015peking,peng2017deep,dozat2018simpler,wang2018neural,fernandez2020transition}, the rest are second-order parsers \cite{martins2014priberam,cao2017quasi,wang2019second}, higher-order parsing remains under-explored yet. The reason for this issue is that modeling higher-order features with exact inference is NP-hard.
Higher-order features have been widely exploited in syntactic dependency parsing \cite{carreras2007experiments,koo2010efficient,ma2012fourth,ji2019graph,zhang2020efficient}.
However, most of the previous algorithms for higher-order syntactic dependency tree parsing are not applicable to semantic dependency graph parsing.

Graph neural networks (GNNs) have been demonstrated to be an effective tool for solving NP-hard problems with approximate inference in many graph learning tasks \cite{prates2019learning,zhang2019deep,zhao2021distributed}.
GNNs aggregate higher-order information in a similar incremental manner: one GNN layer encodes information about immediate neighbors and $K$ layers encode $K$-order neighborhoods (i.e., information about nodes at most $K$ hops aways).

Inspired by the success of GNNs, we investigate building a higher-order semantic dependency parser (HOSDP) by applying GNNs in this paper. Instead of explicitly extracting higher-order features from intermediate parsing graphs, we aggregate higher-order information and bring global evidence into decoders' final decision by stacking multiple GNN layers. We extend the biaffine parser \cite{dozat2018simpler} and employ it as the vanilla parser to produce an initial adjacency matrix (close to gold) since there is no graph structure available during testing. Two GNNs variants, Graph Convolutional Network (GCN) \cite{kipf2016semi} and Graph Attention Network (GAT) \cite{velivckovic2017graph} have been investigated in HOSDP.
Our model has been evaluated on SemEval 2015 Task 18 English Dataset which contains three semantic dependency formalisms (DM, PAS, and PSD).
Experimental results show that HOSDP outperforms the previous best one. In addition, HOSDP shows more advantage over the baseline in the longer sentence and PSD formalism (appearing linguistically most fine-grained).
Our code is publicly available at \url{https://github.com/LiBinNLP/HOSDP}.

\section{Related work}
In this section, the studies of higher-order syntactic dependency parsing, semantic dependency parsing and GNNs will be summarized as follows.
\subsection{Higher-order Syntactic Dependency Parsing}
Higher-order parsing has received a lot of attention in the syntactic dependency parsing.
\citet{carreras2007experiments} presented a second parser which incorporate grand-parental relationships in the dependency structure.
\citet{koo2010efficient} developed a third-order parser. They introduced grand-sibling and tri-sibling parts.
\citet{ma2012fourth} developed a forth-order parser which utilized  grand-tri-sibling parts for fourth-order dependency parsing.
\citet{ji2019graph} used graph neural networks to captures higher-order information for syntactic dependency parsing, which was closely related to our work.
\citet{zhang2020efficient} presented a second-order TreeCRF extension to the biaffine parser.

Higher-order features have been widely exploited in syntactic dependency parsing. However, most of the previous algorithms for higher-order syntactic dependency tree parsing are not applicable to semantic dependency graph parsing.

\subsection{Semantic Dependency Parsing}
Several SDP models are presented in the recent years. Their parsing mechanisms are either transition-based or graph-based. Most of them are first-order parsers.
\citet{wang2018neural} presented a neural transition-based parser, using a variant of list-based arc-eager transition algorithm for dependency graph parsing.
\citet{lindemann2020fast} developed a transition-based parser for \emph{Apply-Modify} dependency parsing. They extended the stack-pointer model to predict transitions.
\citet{fernandez2020transition} developed a transition-based parser, using Pointer Network to choose a transition between \emph{Attach-p} and \emph{Shift}.
More recently, there has been a predominance of purely graph-based SDP models.
\citet{dozat2018simpler} extended the LSTM-based syntactic parser of
\citet{dozat2017stanford} to train on and generate SDG.
\citet{kurita2019multi} developed a reinforcement learning-based approach that iteratively applies the syntactic dependency parser to build a DAG structure sequentially.
\citet{jia2020semi} presented a semi-supervised model based on Conditional Random Field Autoencoder to learn a dependency graph parser.
\citet{he2020establishing} significantly improved the performance by introducing contextual string embeddings (called Flair).

Higher-order SDP receives less attention.
\citet{martins2014priberam} developed a second-order parser which employ a feature-rich linear model to incorporate first and second-order features (arcs, siblings, grandparents and co-parents). $AD^3$ algorithm was employed for approximate decoding.
\citet{cao2017quasi} presented a quasi-second-order parser and used a dynamic programming algorithm (called Maximum Subgraph) for exact decoding.
\citet{wang2019second} extended the parser \cite{dozat2018simpler} and managed to add second-order information for score computation and then apply either mean-field variational inference or loopy belief propagation for approximate decoding.

Higher-order parsing remains under-explored yet. The reason for this issue is that modeling higher-order features with exact inference is NP-hard.

\subsection{Graph Neural Networks}
Recent years have witnessed great success from GNNs in graph learning.
Two main families of GNNs have been proposed, i.e., spectral methods and spatial methods.
GCNs \cite{kipf2016semi} is a spectral-based method, which learns node representation based on graph spectral theory.
GAT \cite{velivckovic2017graph} is a spatial-based method, which introduces the multi-head attention mechanism to learn different attention scores for neighbors when aggregating information.

GNNs have been demonstrated to be an effective tool for solving NP-hard problems with approximate inference in many graph learning tasks.
\citet{prates2019learning} utilized GNNs to solve the Traveling Salesperson Problem, a highly relevant NP-Complete problem.
\citet{zhang2019deep} used GNNs to solve NP-hard assignment problems in image feature matching task.
\citet{zhao2021distributed} used GCNs to solve maximum weighted independent set problem, which is NP-hard. Inspired by the success of GNNs, we investigate building a higher-order semantic dependency parser by applying GNNs.

\begin{figure*}[!ht]
        \centering
        \includegraphics[width=0.8\textwidth]{imgs/HOSDP.pdf}
        \caption{Overall architecture of the proposed HOSDP.}
        \label{fig:our_model}
\end{figure*}
\section{HOSDP}

HOSDP is a parser extends \citet{dozat2018simpler}. An overview of HOSDP is shown as Figure \ref{fig:our_model}. Given sentence $s$ with $n$ words $[w_1, w_2,\dots,w_n]$,  there are three steps to parse it as an SDG. Firstly, the sentence will be parsed with a vanilla SDP parser,
producing an initial SDG. Secondly, the contextualized word representations output by long short-term memory (BiLSTM) and adjacency matrix obtained from the initial SDG will be fed into the GNN encoder, to obtain node representations which aggregate higher-order information. Finally, MLP will be used to get the hidden state representation, and then decoded by biaffine classifier to predict edge and label.

\subsection{Vanilla Parser}
We use biaffine parser \cite{dozat2018simpler} as the vanilla parser. The sentence $s$ will be parsed by the vanilla parser to obtain initial adjacency matrix.
\begin{equation}
(\Tilde{A}, \Tilde{G}) = VanillaParser(W, F)
\end{equation}
where $\Tilde{A}$ is the initial adjacency matrix; $\Tilde{G}$ is the initial SDG; $W$ denotes $n$ words and $F$ denotes features of words.

\subsection{Encoder}
We concatenate word and feature embeddings, and feed them into a BiLSTM to obtain contextualized representations.

\begin{equation}
x_i=e_i^{(word)}\oplus e_i^{(feat)}
\end{equation}

\begin{equation}
O=BiLSTM(X)
\end{equation}
where $x_i$ is the concatenation ($\oplus$) of the word and
feature embeddings of word $w_i$, $X$ represents $[x_1,x_2,\dots,x_n]$. $O=[o_1,o_2,\dots,o_n]$ is the contextualized representations of sequence $X$.

Then we employ $K$-layer GNNs to capture higher-order information by aggregating representation of $K$-order neighborhoods. Node embedding matrix $R^{(k)}$ in $k^{th}$-layer is computed as Equation \ref{GNNLayer}:

\begin{equation}
R^{(k)}=\emph{GNNLayer}^{(k-1)}(R^{(k-1)}, \Tilde{A}) \label{GNNLayer}
\end{equation}

When \emph{GNNLayer} is implemented in \emph{GCN}, the representation of node $i$ in $k^{th}$ layer $r_i^{(k)}$ is computed as Equation \ref{GCNLayer}

\begin{equation}
r_i^{(k)}=\sigma \left(W\sum_{j\in N(i)} r_j^{(k-1)}+B r_i^{(k-1)}\right)\label{GCNLayer}
\end{equation}
where $W$ and $B$ are parameter matrices; $N(i)$ are neighbors of node $i$; $\sigma$ is active function (we use ReLU); $r_i^{(0)} = o_i$.

When \emph{GNNLayer} is implemented in \emph{GAT}, $r_i^{(k)}$ is computed as Equation \ref{GATLayer}:
\begin{equation}
r_i^{(k)}=\sigma \left(W\sum_{j\in N(i)} a_{ij}^{(m)(k-1)}r_j^{(k-1)}+B r_i^{(k-1)}\right)\label{GATLayer}
\end{equation}
where $a_{ij}^{(m)(k-1)}$ is attention coefficient of node $i$ to its neighbour
$j$ in attention head $m$ at $(k-1)^{th}$ layer.

Although higher-order information is important, GNNs would suffer from the over-smoothing problem when the number of layer is too many. So we stack 3 layers with the past experience.

\subsection{Decoder}
Decoder has two modules: edge existence prediction module and edge label prediction module. For each of the two modules, we use \emph{MLP}
to split the final node representation $R=[r_1,r_2,\dots, r_n]$ into two parts—a head representation, and a dependent representation, as shown in Equation \ref{edge-head} to \ref{label-dep}:
\begin{equation}
h_i^{(edge-head)}=MLP^{(edge-head)}(ri)\label{edge-head}
\end{equation}

\begin{equation}
h_i^{(label-head)}=MLP^{(label-head)}(ri)\label{label-head}
\end{equation}

\begin{equation}
h_i^{(edge-dep)}=MLP^{(edge-dep)}(ri)\label{edge-dep}
\end{equation}

\begin{equation}
h_i^{(label-dep)}=MLP^{(label-dep)}(ri)\label{label-dep}
\end{equation}

We can then use biaffine classifiers (as Equation \ref{biaff}), which are
generalizations of linear classifiers
to include multiplicative interactions between two
vectors—to predict edges and labels, as Equation \ref{edge} and \ref{label} :

\begin{equation}
Biaff(x_1, x_2)=x_1^TUx_2 + W(x_1\oplus x_2)+b \label{biaff}
\end{equation}

\begin{equation}
s_{i,j}^{(edge)}=Biaff^{(edge)}(h_i^{(edge-dep)}, h_j^{(edge-head)})\label{edge}
\end{equation}

\begin{equation}
s_{i,j}^{(label)}=Biaff^{(label)}(h_i^{(label-dep)}, h_j^{(label-head)})\label{label}
\end{equation}
where $s_{i,j}^{(edge)}$ and $s_{i,j}^{(label)}$ are scores of
edge existence and edge label between the word $w_i$ and $w_j$.
$U$, $W$ and $b$ are learned parameters
of biffine classifier. For edge existence prediction module,
$U$ will be $(d*1*d)$-dimensional,  so that $s_{i,j}^{(edge)}$
will be a scalar. For edge label prediction, if the parser is unlabeled,
$U$ will be $(d*1*d)$-dimensional, so that $s_{i,j}^{(label)}$ will be a scalar.
If the parser is labeled, $U$ will be $(d*c*d)$-dimensional,
where $c$ is the number of labels, so that $s_{i,j}^{(label)}$ is a vector
that represents the probability distributions of each label.

The unlabeled parser scores each edge between
pairs of words in the sentence—these scores
can be decoded into a graph by keeping only edges
that received a positive score. The labeled parser scores
every label for each pair of words, so we simply
assign each predicted edge its highest-scoring label
and discard the rest.

\begin{equation}
\hat{y}_{i,j}^{(edge)} = \{s_{i,j}^{(edge)}>0\}
\end{equation}

\begin{equation}
\hat{y}_{i,j}^{(label)} = \arg\max s_{i,j}^{(label)}
\end{equation}

\subsection{Learning}
We can train the system by summing the losses from the two modules,
back propagating error to the parser.
Cross entropy function is used as as the loss function, which is computed as Equation \ref{crossentropy}:
\begin{equation}
CE(p,q)=-\sum_{x}p(x)\log q(x) \label{crossentropy}
\end{equation}

We define the loss function of edge existence prediction module
and edge label prediction module:
\begin{equation}
\mathcal{L}^{(edge)}(\theta_2)=CE(\hat{y}_{i,j}^{(edge)}, y_{i,j}^{(edge)})
\end{equation}

\begin{equation}
\mathcal{L}^{(label)}(\theta_1)=CE(\hat{y}_{i,j}^{(label)}, y_{i,j}^{(label)})
\end{equation}
where $\theta_1$ and  $\theta_2$ are the parameters of two modules.

Then the Adaptive Moment Estimation (Adam) method is used to optimize
the summed loss function $\mathcal{L}$:
\begin{equation}
\mathcal{L}=\lambda \mathcal{L}^{(edge)} + (1-\lambda) \mathcal{L}^{(label)}
\end{equation}
where $\lambda$ is a tunable interpolation constant $\lambda \in (0,1)$.

\section{Experiments}
\subsection{Dataset}
To evaluate the performance of HOSDP, we conduct experiments on the SemEval 2015 Task 18 English datasets \cite{oepen2015semeval}, where all sentences
are annotated with three different formalisms:
DELPH-IN MRS (DM), Predicate-Argument Structure (PAS)
and Prague Semantic Dependencies (PSD).
We use the same dataset split as in
previous approaches \cite{almeida2015lisbon,du2015peking}
with 33,964 training sentences from
Sections 00-19 of the Wall Street Journal corpus
\cite{marcus1993building}, 1,692 development sentences
from Section 20, 1,410 sentences from Section 21
as in-domain (ID) test set, and 1,849 sentences sampled
from the Brown Corpus \cite{francis1982frequency}
as out-of-domain (OOD) test data. For the evaluation,
we use the evaluation script used in \cite{wang2019second},
reporting labelled F-measure scores (LF1) (including ROOT arcs)
on the ID and OOD test sets for each formalism as well as the macro-average
over the three of them.

\subsection{Hyperparameters}
The hyperparameter configuration for our final
system is given in Appendix \ref{Hyperparameters}.
We use 100-dimensional pretrained GloVe embeddings. Only
words or lemmas that occurred 7 times or more
are included in the word and lemma embedding
matrix—including less frequent words appeared to
facilitate overfitting. Character-level word embeddings
are generated using a one-layer unidirectional
LSTM that convolved over three character
embeddings at a time, whose final state is linearly
transformed to be 100-dimensional.

We train the model for at most 100 epochs and terminated training early after 20 epochs with no improvement on the development set.

\begin{table*}[h]
\centering
\begin{tabular}{lccccccccc}
\toprule
Models & \multicolumn{2}{c}{DM} &  \multicolumn{2}{c}{PAS} &  \multicolumn{2}{c}{PSD} & \multicolumn{2}{c}{Avg}\\ & ID & OOD & ID & OOD & ID & OOD & ID & OOD\\

\midrule
Du et al. \shortcite{du2015peking} & 89.1 & 81.8 &91.3 &87.2 &75.7 &73.3 &85.3 &80.8   \\
A\&M \shortcite{almeida2015lisbon} & 88.2 & 81.8 & 90.9 & 86.9 & 76.4 & 74.8 & 85.2 & 81.2  \\
PTS17 \shortcite{peng2017deep}: Basic & 89.4 & 84.5 & 92.2 & 88.3 & 77.6 & 75.3 & 86.4 & 82.7\\
PTS17 \shortcite{peng2017deep}: Basic & 90.4 & 85.3 & 92.7 & 89.0 & 78.5 & 76.4 & 87.2 & 83.6\\
WCGL \shortcite{wang2018neural} & 90.3 & 84.9 & 91.7 & 87.6 & 78.6 & 75.9 & 86.9 & 82.8  \\
D\&M \shortcite{dozat2018simpler}: Basic &91.4 & 86.9 & 93.9 & 90.8 & 79.1 & 77.5 & 88.1 & 85.0   \\
MF \shortcite{wang2019second}: Basic & 93.0 & \textbf{88.4} & 94.3 & 91.5 & 80.9 & 78.9 & 89.4 & 86.3   \\
LBP \shortcite{wang2019second}: Basic & 92.9 & \textbf{88.4} & 94.3 & 91.5 & 81.0 & 78.8 & 89.4 & 86.2\\
Lindemann et al. \shortcite{lindemann2019compositional}: Basic & 91.2 & 85.7 & 92.2 & 88.0 & 78.9 & 76.2 & 87.4 & 83.3 \\
Semantic Pointer \shortcite{fernandez2020transition}: Basic & 92.5 & 87.7 & 94.2 & 91.0 & 81.0 & 78.7 & 89.2 & 85.8\\
HOSDP (GCN): Basic & \textbf{93.3} & 88.0 &\textbf{94.8} & 91.1 & \textbf{85.6} &\textbf{83.6}&\textbf{91.2}&\textbf{87.6}\\
HOSDP (GAT): Basic & {93.0} & {87.9} &\textbf{94.8} &\textbf{91.6} & \textbf{85.4} &\textbf{83.3}&\textbf{91.1}&\textbf{87.6}\\
\midrule
D\&M \shortcite{dozat2018simpler}: \scriptsize{+Char+Lemma}& 93.7 & 88.9 & 93.9 & 90.6 & 81.0 & 79.4 & 89.5 & 86.3\\
MF \shortcite{wang2019second}: \scriptsize{+Char+Lemma} & 94.0 & 89.7 & 94.1 & 91.3 & 81.4 & 79.6 & 89.8 & 86.9\\
LBP \shortcite{wang2019second}: \scriptsize{+Char+Lemma} & 93.9 & 89.5 & 94.2 & 91.3 & 81.4 & 79.5 & 89.8 & 86.8\\
Jia et al. \shortcite{jia2020semi}: \scriptsize{+Lemma} & 93.6 & 89.1 & - & - & - & - & - & - \\
Semantic Pointer \shortcite{fernandez2020transition}: \scriptsize{+Char+Lemma} & 93.9 & 89.6 & 94.2 & 91.2 & 81.8 & 79.8 & 90.0 & 86.9\\

HOSDP (GCN): \scriptsize{+Char+Lemma} & \textbf{94.2} & \textbf{90.1} & \textbf{ 94.9} & \textbf{91.4} &\textbf{86.4} & \textbf{84.9} & \textbf{91.8} & \textbf{88.8}\\
HOSDP (GAT): \scriptsize{+Char+Lemma} & \textbf{94.4} & \textbf{89.9} & \textbf{95.0} & \textbf{91.8} &\textbf{86.2} & \textbf{84.6} & \textbf{91.9} & \textbf{88.8}\\
\midrule
Lindemann et al. \shortcite{lindemann2019compositional}: \scriptsize{+BERT\tiny{large}} & 94.1 & 90.5 &  94.7 & 92.8 & 82.1 & 81.6 & 90.3 & 88.3\\
Lindemann et al. \shortcite{lindemann2020fast}: \scriptsize{+BERT\tiny{large}} & 93.9 & 90.4 & 94.7 & 92.7 &  81.9 & 81.6 & 90.2 & 88.2\\
Semantic Pointer \shortcite{fernandez2020transition}: \scriptsize{+Char+Lemma+BERT\tiny{base}} & 94.4 & 91.0 & 95.1 & 93.4 & 82.6 & 82.0 & 90.7 & 88.8\\
He et al. \shortcite{he2020establishing}: \scriptsize{+Char+Lemma+BERT\tiny{base}+Flair} & 94.6 & 90.8 & \textbf{96.1} & \textbf{94.4} & 86.8 & 79.5 & 92.5 & 88.2\\
HOSDP (GCN): \scriptsize{+Char+Lemma+BERT\tiny{base}}& \textbf{95.1} & \textbf{91.1} & 95.7& 93.2 & \textbf{87.7}&\textbf{87.3}&\textbf{92.8}&\textbf{90.5}\\
HOSDP (GAT): \scriptsize{+Char+Lemma+BERT\tiny{base}} & \textbf{95.3} & \textbf{91.9} & 96.0 & 94.3 & \textbf{87.0}&\textbf{86.7}&\textbf{92.8}&\textbf{91.0}\\
\bottomrule

\end{tabular}
\caption{Comparison of labeled F1 scores achieved by our model and previous state-of-the-art. The F1 scores of Baseline and our models are averaged over 5 runs.  ID denotes the in-domain (WSJ) test set and OOD denotes the out-of-domain (Brown) test set. +Char, +Lemma, +BERT, and +Flair mean augmenting the token embeddings with character-level, lemma embeddings, BERT embeddings, and Flair embeddings. Semi-SDP only shows the full-supervised result on DM formalism.}
\label{main_result}
\end{table*}

\subsection{Baseline}
We compare HOSDP with previous state-of-the-art
parsers in Table \ref{main_result}. \citet{du2015peking} is a
hybrid model. A\&M is from \citet{almeida2015lisbon}.
WCGL \cite{wang2018neural} is a neural transition-based model.
PTS17 proposed by \citet{peng2017deep} and
Basic is single task parsing while Freda3 is a multitask
parser across three formalisms.
D\&M \cite{dozat2018simpler} is a
first-order graph-based model. MF and LBP \cite{wang2019second}
are a second-order model using mean field variational inference
or loopy belief propagation.
\citet{lindemann2019compositional} and \citet{lindemann2020fast}
are compositional semantic parser for SDP and abstract meaning
representation. Semantic Pointer \cite{fernandez2020transition}
is a transition-based model using Pointer Network. \citet{jia2020semi}
is a semi-supervised parser, only the full-supervised result on DM
formalism is shown in their paper.
\citet{he2020establishing} uses not only BERT but also contextual string embeddings (called Flair).

\subsection{Main Results}
We group SDP models in three blocks depending on the embeddings provided to the models:
(1) just basic pre-trained GloVe word embeddings and POS tag embeddings (Basic),
(2) character and pre-trained lemma embeddings augmentation (+Char+Lemma) and
(3) pre-trained BERT embeddings augmentation (+Char+Lemma+BERT).

Table \ref{main_result} presents the comparison of HOSDP
and related studies on the test sets of SemEval 2015 Task 18 English datasets.
From the results, we have the following observations:
\begin{itemize}
    \item In the basic setting, HOSDP achieves 1.8 and 1.3 averaged LF1 improvements on the ID and OOD test set over the previous best parsers.

    \item Adding both the character-level and the lemma embeddings, most models improve performance quite a bit generally.

    HOSDP leads to 1.9 and 1.9 averaged LF1 improvements over the previous best parsers on the ID and OOD test sets.

    \item Adding BERT embedding pushes performance even higher generally.
    HOSDP outperforms the previous best parsers by 0.3 and 2.2 average LF1 improvements on ID and OOD test sets, respectively.

    \item HOSDP makes significant improvements on the PSD formalism,
    with 4.6 and 4.7 LF1 improvements in the basic setting,
    4.6 and 5.1 LF1 improvements when character-level and lemma embeddings are added, 0.9 and 5.3 LF1 improvements when BERT embeddings are added on the ID and OOD test sets, respectively.

    \item The LF1 scores of all parsers on the PSD is lower than the other two formalisms. Table \ref{statistics} shows part of contrastive statistics of three formalisms. We have noticed that PSD  formalism appears linguistically most fine-grained because it contains the most semantic labels and frames \cite{oepen2015semeval}. This makes PSD  more challenging to predict. However, HOSDP performs better than other first-order and second-order parsers, suggesting that higher-order information is beneficial to SDP.

    \item The performances of HOSDP (GCN) and HOSDP (GAT) are almost the same in three embedding settings, demonstrating that both GCNs and GAT are capable to capture higher-order information.
\end{itemize}

In summary, outstanding performances of HOSDP have demonstrated that higher-order information can bring considerable accuracy gains in SDP. In addition, GNNs are capable to capture higher-order information and are effective for higher-order modeling.

\begin{table}[h]
\centering
\begin{tabular}{lcccccc}
\toprule
 & \multicolumn{2}{c}{DM} & \multicolumn{2}{c}{PAS} & \multicolumn{2}{c}{PSD}
\\ & ID & OOD & ID & OOD & ID & OOD\\

\midrule
\# labels & 59 & 47  & 42 & 41 & 91 & 74 \\
\# frames & 297 & 172 & - & - & 5426 & 1208 \\

\bottomrule
\end{tabular}
\caption{Contrastive statistics of three semantic formalisms.}
\label{statistics}
\end{table}

\begin{figure*}[!ht]
	\centering
	\vspace{-0.35cm}
	\subfigbottomskip=2pt
	\subfigcapskip=-1pt
	\subfigure[DM.ID]{
		\label{DM.ID}
		\includegraphics[width=0.32\textwidth]{imgs/DM.ID.pdf}}
	\subfigure[PAS.ID]{
		\label{PAS.ID}
		\includegraphics[width=0.32\textwidth]{imgs/PAS.ID.pdf}}
	\subfigure[PSD.ID]{
		\label{PSD.ID}
		\includegraphics[width=0.32\textwidth]{imgs/PSD.ID.pdf}}
	\subfigure[DM.OOD]{
		\label{DM.OOD}
		\includegraphics[width=0.32\textwidth]{imgs/DM.OOD.pdf}}
	\subfigure[PAS.OOD]{
		\label{PAS.OOD}
		\includegraphics[width=0.32\textwidth]{imgs/PAS.OOD.pdf}}
	\subfigure[PSD.OOD]{
		\label{PSD.OOD}
		\includegraphics[width=0.32\textwidth]{imgs/PSD.OOD.pdf}}
	\subfigure{
		\label{plot_lagend}
		\includegraphics[width=0.4\textwidth]{imgs/plot_lagend.pdf}}
	\caption{LF1 of different sentence lengths in 3 formalisms.
	$\star$-T represents that only the POS tag is used as feature.
	$\star$-TCL represents that the POS tag, character-level and
	lemma embeddings are used as features. $\star$-TCLB represents that
	the POS tag, character-level, lemma and BERT embeddings
	are used as features.}
	\label{sent_len_fig}
\end{figure*}

\subsection{Analysis}

\subsubsection*{Performance on Different Sentence Length}
We want to study the impact of sentence lengths.
The ID and OOD test sets of the three formalisms
are split with 10 tokens range. The ID test set has 6 groups and OOD
has 7. HOSDP (GCN) and biaffine parser are evaluated on them.
The results on diffenent groups are shown in Figure \ref{sent_len_fig}.

The results show that HOSDP outperforms the biaffine parser on different groups with the same embedding configuration, except for slightly lower on the first group (0-10 tokens) on ID test set of PAS formalism.
Furthermore, HOSDP that only utilizes POS tag embedding outperforms biaffine parse that uses POS tag, character-level, and lemma embeddings, when sentences get longer, especially when sentences are longer than 30. It turns out that higher-order information is favorable for longer sentences since higher-order dependency relationships are more prevalent in longer sentences.

\subsubsection*{Case study}
We provide a parsing example to show why HOSDP benefits from higher-order information. Figure \ref{sent_parse_fig} shows the parsing results of biaffine parser (Figure \ref{biaff_result}) and HOSDP (GCN) (Figure \ref{HOSDP_result}), for sentence (sent\_id=40504035, in OOD of PSD formalism): \emph{There is never enough corn, peas or strawberries}. Both of two parsers are trained in the basic embedding setting.

In the gold annotation, three words \emph{corn}, \emph{peas} and \emph{strawberries} are three members of the disjunctive word \emph{or}. In addition, the word \emph{enough} has three dependency edges labeled \emph{EXT} with them.
In the result of biaffine parser, only dependency edge between \emph{enough} and \emph{corn} is identified, the remaining two are not.
In HOSDP (GCN), given the initial SDG predicted by biaffine parser, words \emph{corn}, \emph{peas} and \emph{strawberries} aggregate the higher-order information of \emph{or} and \emph{is} ($1^{st}$-order), \emph{there} ($2^{nd}$-order) and \emph{ROOT} ($3^{rd}$-order). The word \emph{enough} aggregate the higher-order information of the \emph{corn} ($1^{st}$-order), \emph{is} and \emph{or} ($2^{nd}$-order), \emph{there} ($3^{rd}$-order), and \emph{ROOT} ($4^{th}$-order). Dependent-word \emph{enough} and three head-words \emph{corn}, \emph{peas} and \emph{strawberries} aggregate information of four common words (\emph{ROOT}, \emph{There}, \emph{is} and \emph{or}). Therefore the representations of them with higher-order information bring global evidence into decoders' final decision.
As a result, it is effortless for HOSDP to identify that there are also two dependency edges labeled \emph{EXT} between dependent-word \emph{enough} and head-words \emph{peas} and \emph{strawberries}.

\begin{figure}[!ht]
	\centering
	\vspace{-0.35cm}
	\subfigtopskip=2pt
	\subfigbottomskip=2pt
	\subfigcapskip=-2pt
	\subfigure[Parsing result of biaffine parser]{
		\label{biaff_result}
		\includegraphics[width=0.48\textwidth]{imgs/brat-biaff-40304035.pdf}}
	\subfigure[Parsing result of HOSDP (GCN)]{
		\label{HOSDP_result}
		\includegraphics[width=0.48\textwidth]{imgs/brat-gold-40304035.pdf}}
	\caption{Parsing results of biaffine parser and HOSDP,
    for the sentence (sent\_id=40504035 in OOD of PSD formalism). Some irrelevant dependency edges are hidden. }
	\label{sent_parse_fig}
\end{figure}

\section{Conclusions}
We propose a higher-order semantic dependency parser, which employs the GNNs to capture higher-order information. Experimental results show that HOSDP outperforms previous best one on the SemEval 2015 Task 18 English datasets. In addition, HOSDP shows more advantage in longer sentence and complex semantic formalism. In the future, we would like to apply graph structure learning models to learn adjacency matrix, rather than depending on the results of vanilla parser.

\clearpage
\bibliographystyle{named}
\footnotesize

redpajama_set_name: RedPajamaArXiv


He has a vision for New York City to see people saved and come to the knowledge of Jesus Christ as their Savior. Central to the development of this ministry which started as a Home Group in his home in the mid 1970's, he is the leading figure in this ministry which now stretches to areas in Africa, Europe, the Caribbean and Central America.
His dedication and devotion to the Word of God has made him an instrument of service to this ministry, and to many others who have sought his counsel and wisdom to guide their paths. Tirelessly preaching 365 days per year, his inception of early morning prayers in the church has been a blessing to the lives of everyone that it has touched.
Bishop Delmadge served as the first Pastor of Christian Heritage Church in 1987, while still working fulltime at his secular job. He became the owner and president of United Medical Imaging, Inc., an X-Ray installation and servicing company in 1992. He has since resigned from that company and has dedicated his full efforts to the ministry. As a member of International Christian Center (ICC), he was first an Elder, then a Pastor in that ministry, ordained at ICC and working closely with Dr. N. Benjamin Crandall, his spiritual mentor.
He has been a member of the Board of Trustees at Zion Bible College since 1995 and currently serves as the Chairman of the Board for Christian Heritage Ministries and the Headmaster of Christian Heritage Academy. Bishop Delmadge has been eloquently schooled in the Gospel, and for his work in the ministry and dedication to the work of Christ, has been recognized with an Honorary Degree in Ministerial Studies from Zion Bible College.
Today, he and his wife Merna are pioneering the new outreach in Central Florida. He is currently ministering at Christian Heritage of Kissimmee in Central Florida with plans to reach Tampa, Oviedo and Clermont. His mission has also targeted other areas as he plans to start churches in Atlanta, Georgia, as well as other neighboring states. His dream to see the Gospel reach the ends of the Earth, is compelling enough for him to see a church in every block and every home. While he works to bring the Good News of salvation home to everyone, he has also dedicated his life to the planting of churches everywhere. In all of these activities, given an invitation, he will still meet you at your house, to bring the reality of the Gospel of Christ home to you.