A language-independent representation of meaning is one of the most coveted dreams in Natural Language Understanding. With this goal in mind, several formalisms have been proposed as frameworks for meaning representation in Semantic Parsing. And yet, the dependencies these formalisms share with respect to language-specific repositories of knowledge make the objective of closing the gap between highand low-resourced languages hard to accomplish. In this paper, we present the BabelNet Meaning Representation (BMR), an interlingual formalism that abstracts away from language-specific constraints by taking advantage of the multilingual semantic resources of BabelNet and VerbAtlas. We describe the rationale behind the creation of BMR and put forward BMR 1.0, a dataset labeled entirely according to the new formalism. Moreover, we show how BMR is able to outperform previous formalisms thanks to its fully-semantic framing, which enables top-notch multilingual parsing and generation. We release the code at https: //github.com/SapienzaNLP/bmr . Natural Language Understanding (NLU) enables machines to understand human language. A key enabling task in NLU is that of Semantic Parsing, whose longed-for dream is that of developing a formalism that can be used as an interlingual representation of meaning, i.e., one that, independently of the language, can explicitly embed sentence meaning into a machineand human-readable form (Navigli, 2018). To this end, different formalisms such as Abstract Meaning Representation (Banarescu et al., 2013, AMR), Universal Conceptual Cognitive Annotation (Abend and Rappoport, 2013, UCCA) and Universal Meaning Representation (Van Gysel et al., 2021, UMR), have been proposed over the years. formalism for Semantic Parsing, being widely applied to a variety of areas of NLP, such as Machine Translation (Song et al., 2019), Question Answering (Lim et al., 2020; Bonial et al., 2020b; Kapani-pathi et al., 2021), Human-Robot Interaction (Bo-nial et al., 2020a), Text Summarization (Hardy and Vlachos, 2018; Liao et al., 2018) and Information Extraction (Rao et al., 2017). The primary precept of AMR is that different sentences carrying the same meaning should have the same graph representation. Nonetheless, a few inherent properties of AMR make it inappropriate for the purpose of providing a language-agnostic representation of meaning. In fact, nodes within AMR graphs are represented by means of either English lemmas or OntoNotes frames (Hovy et al., 2006) which, in turn, are based on PropBank (Kingsbury and Palmer, 2002). The issue with lemmas is that they are merely surface forms devoid of semantics, whereas, with respect to frames, even though analogous repositories exist in other languages such as AnCora for Spanish (Aparicio et al., 2008) or the Chinese PropBank (Xue and Palmer, 2009), they are not mutually interlinked, hence making the cross-lingual application of AMR arduous to achieve (Conia et al., 2021). Against this background, we follow the ideas put forward by Navigli et al. (2022) and develop the BabelNet Meaning Representation (BMR), a formalism providing the building blocks for a language-agnostic representation of meaning by exploiting the wealth of multilingual knowledge contained in BabelNet (Navigli and Ponzetto, 2010; Navigli et al., 2021) 1 and VerbAtlas (Di Fabio et al., 2019) 2 . In outline, the main contributions of this paper are as follows: (i) we introduce BMR, a new Semantic Parsing formalism that can be used as an interlingua, (ii) we produce BMR 1.0, i.e., the first 1 https://babelnet.org/ 2 https://verbatlas.org/ 1727 lexical-semantic dataset annotated according to the BMR formalism, (iii) we create and release models that can generate BMR graphs from text and text from BMR graphs in English, German, Spanish, and Italian, and (iv) we describe a sound experimental setup to show how, thanks to its fully semantic framing, BMR outdoes previous formalisms in both preserving and encoding textual information, as well as in being used as an interlingua in downstream tasks such as Machine Translation. Even though the vast majority of formalisms for Semantic Parsing have been designed with English in mind, several approaches have attempted to narrow the gap between English and other languages. For instance, Universal Conceptual Cognitive Annotation (Abend and Rappoport, 2013, UCCA) was proposed as a cross-lingual annotation formalism in which words in a sentence are connected using semantic relations not tied to specific languages. And yet, while UCCA reflects the semantic relations between nodes via a set of coarse-grained roles, it represents concepts by means of simple lemmas, hence preventing an abstraction from language-specific constraints. Parallel Meaning Bank (Abzianidze et al., 2017, PMB), an approach based on the Discourse Representation Theory (Kamp and Reyle, 1993, DRT), 3 also emerged. In PMB, English sentences are parsed with labels that are automatically projected to non-English translations. PMB too, however, cannot be seen as a unified interlingual representation, since it uses English-specific meaning repositories. As regards Abstract Meaning Representation (Banarescu et al., 2013, AMR), instead, several approaches have tried to adapt it for cross-lingual use. As a case in point, Xue et al. (2014) analyzed the viability of tailoring the AMR formalism to fit other languages by making use of language-specific repositories similar to PropBank (Aparicio et al., 2008; Xue and Palmer, 2009). 4 On a different note, Damonte and Cohen (2018) and Blloshmi et al. (2020) attempted to adopt AMR as an interlingual formalism, despite its English-centric nature, by assuming that the AMR graph of an English sentence is also representative of translations of that sentence in other languages. Once again, these 3 DRT is a framework that embeds the semantics of an utterance employing a formal logic semantic structure. approaches testify to the limits of AMR as an interlingua, given the drawbacks of dealing with structural divergences among different languages. In recent years, Zhu et al. (2019) have recommended abstracting the AMR formalism away in order to reduce its language-specific complexity by preserving just the predicate roles and relations that constitute the core semantic information of sentences. Conversely, rather than decreasing the complexity of AMR, the Universal Meaning Representation (Van Gysel et al., 2021, UMR) extends it by including new features that render the formalism less tied to a specific language. In particular, UMR enriches the verbal predicates with information about grammatical aspect and scope, while introducing temporal and modal dependencies at the document level. Finally, it enhances AMR to use it as a cross-lingual formalism by employing language-specific repositories and relations. Yet, the focus of UMR is that of providing languages with the necessary resources to parse texts, rather than being an interlingual representation. In contrast to previous approaches, and thanks to the multilingually-shared word meanings and semantic roles taken from the interlinked repositories of BabelNet (Navigli et al., 2021) and VerbAtlas (Di Fabio et al., 2019), we put forward BMR, a formalism that fully detaches from syntax and thus stands as a lexical-semantic representation that is able to bring different languages together. To accomplish the goal of an interlingual meaning representation, we disconnect our formalism from language-specific constraints of any kind. To this end, we draw on resources that inherently connect word meanings and predicate-argument structures across languages, i.e., BabelNet and VerbAtlas. BabelNet (Navigli et al., 2021) is a multilingual encyclopedic dictionary and semantic knowledge base in which concepts are represented as synsets (sets of synonyms that convey the same meaning), linked via semantic relation edges like hypernymy or meronymy. BabelNet was built by the aggregation of several knowledge resources including WordNet (Fellbaum, 1998), Wikipedia and Wik-tionary, resulting in a remarkable ontology of concepts and named entities covering 500 languages. Given its versatility, which makes it suitable for a wide range of tasks across languages, we employ its most recent version 5.0 as a tool to switch the 1728 Figure 1: AMR graph for the sentence The students and their parents will take the plane at the last minute" . VerbAtlas (Di Fabio et al., 2019) is a manually-curated lexical-semantic inventory that collapses the BabelNet verbal synsets into around 450 semantically-coherent frames, each defining prototypical argument structures via human-readable relationships (e.g. AGENT , THEME ). Thanks to its linkage to BabelNet, VerbAtlas represents the best option for handling predicate-argument relations in BMR in a language-independent manner. Like AMR, BMR embeds the semantics of a sentence in a directed acyclic graph, with nodes and edges connecting them. However, where AMR relies on English lemmas and OntoNotes frames to represent nodes and relations (see Figure 1), BMR disposes of language-specific constraints, and employs multilingual concepts and self-explanatory semantic roles (see Figure 2). 5 In what follows (Sections 4.1 to 4.4), we will describe and detail the features that make BMR stand out with respect to a widely-employed Semantic Parsing formalism such as AMR, as well as their integration into the AMR 3.0 dataset (Knight et al., 2020) to produce the BMR 1.0 dataset. 6 4.1 Self-explanatory Semantic Relations As briefly mentioned in Section 1, AMR derives its coarse-grained frames and argument structures 5 Appendix A details how to read BMR graphs. 6 AMR 3.0 is licensed by LDC at https://catalog. ldc.upenn.edu/LDC2020T02 . For this reason, we do not make the BMR-annotated dataset (BMR 1.0) publicly available, but rather provide tools to convert the original AMR 3.0 dataset, provided its rightful ownership. from the English PropBank section of OntoNotes, a repository which is circumscribed to the English language and that features semantic relations that are both predicate-specific and largely unintelligible without a gloss. For example, in Figure 1, the subgraph representation of students' parents is pivoted on the frame have-rel-role-91 , where the relations :ARG0 , :ARG1 , and :ARG2 identify the first entity, the second entity, and the role of the first entity, respectively. As importantly, even though language-specific repositories similar to PropBank have been used to annotate non-English sentences with structures comparable to those of AMR (Aparicio et al., 2008; Xue and Palmer, 2009), there is not an exact one-to-one mapping between the frames they define, meaning that, e.g., the frame have-rel-role-91 might not be featured in the other inventories. Therefore, with the aim of overcoming language specificity, we replace PropBank with VerbAtlas as an alternative repository of predicate-argument structure information, which, as explained above, inherently accounts for multilingually-shared semantics. To build the BMR 1.0 dataset, we exploit the mapping provided by Di Fabio et al. (2019), which links VerbAtlas frames and arguments to PropBank, and use it to replace the original frames and semantic roles in the AMR 3.0 dataset with those of VerbAtlas (e.g., the frame take-01 corresponds to MOVE_BY_MEANS_OF in VerbAtlas, and its ARG0 to AGENT ). However, this mapping is incomplete and, as a result, several predicates found within AMR 3.0 can not be transitioned directly. Among these, two kinds of predicates can be identified, (i) predicates that OntoNotes labels as verbal, and (ii) non-verbal predicates and special predicates which AMR uses to define special semantic structures (e.g., have-rel-role-91 ). To deal with these predicates, we asked a linguist 7 to create a mapping between PropBank and VerbAtlas for the missing verbal predicates, and, with respect to the others instead, to map them to BMR adapting previous semantic roles and creating new ones to better accommodate their argument structures. 8 4.2 Node Merging Multiword expressions and idioms are rendered word by word in AMR, using node composition. Nevertheless, such an approach is not feasible for an interlingual representation, since the overall meaning of an expression can not, as a general rule, be compositionally inferred from the meanings of its individual words. Therefore, in BMR we make use of the available BabelNet synsets to identify the meaning of a multiword expression or idiom, and hence we represent it with a single node. As a case in point, the idiom at the last minute which, according to Wiktionary, is defined as very close to a deadline or potential crucial event, does not entail that something will happen precisely in the last minute. This exact expression, that in AMR 3.0 is represented using two nodes ( m and l ) as: ( m / minute : mode ( l / last )) appears in BMR as a single node m : ( m / at _ the _ last _ minute / bn :00114428 r ) As a result, we are both able to (i) abstract away from language-specific lexicons making use of concepts connected across languages and, concurrently, to (ii) reduce the graph density, hence easing the computational burden for systems. Another intrinsic limit of AMR as an interlingual representation is that, since the meaning of nodes can only be partially identified using OntoNotes frames, AMR maximizes their usage so as to express as many concepts as possible, even nonverbal ones. The main reason this constitutes an issue is that the OntoNotes frame composition used to define a concept and the concept itself are not semantically equivalent. For example, the concept of student , which AMR represents as a person who studies by means of the connection between the node of person with the OntoNotes frame study-01 , is arguably different from the definition of student as, quoting the BabelNet synset gloss, a learner who is enrolled in an educational institution. Additionally, these language-specific rules are not transferable across languages, and they are not consistent even within AMR itself, as, whenever a verbalization is not viable (AMR does not render professor as a person who professes), the word is included in the graph as it is. In the remainder of this Section, we describe the strategies by means of which we remodel AMR 3.0 to obtain BMR 1.0 employing node merging. Multiword Expression Identification To merge nodes, we must first identify the words or multiword expressions that are represented by several nodes in the AMR graph. In this regard, we proceed by lemmatizing the original sentences in AMR 3.0 using the 3.1 version of the SpaCy software library (Honnibal and Johnson, 2015). At this stage, for each sentence, we check for the longest concatenations of lemmas that match a BabelNet synset lexicalization in BabelNet 5.0. Once the expressions have been identified, we use the automatic AMR aligner of Flanigan et al. (2014) to get the alignments between the tokens in the original sentence (and, consequently, the identified words and multiwords) and the graph nodes. Manual Validation The automatic identification of multiwords can be noisy and lead to poor node merging choices which, in turn, can result in wrong sense attributions. For instance, in the sentence the rest of the world knows the same, the multiword rest of the world is identified, even though its only meaning in BabelNet is that of a team of players from many countries, which is clearly 1730 not appropriate in the reported context. To address this issue, we asked our expert linguist to manually inspect all of the automatically detected multiword instances within the AMR 3.0 dataset in order to maintain, modify or delete them. Graph Conversion Finally, using the multiwords and the alignments derived from the previous steps, we navigate the AMR graphs bottom-to-top and collapse together nodes referring to the same word or multiword expression (i.e., first reducing nodes closer to the graph leaves and then moving towards the graph root). As a result, we move from the original figure of 936 , 769 nodes of AMR 3.0 to 828 , 483 in BMR 1.0, reducing the graph density by a notable 11 . 6% . Even though AMR is able to encode textual information in its semantic structure, its formalism does not account for the inclusion of word components that are crucial for understanding meaning, and that languages express via the grammatical categories of number, tense and aspect. This, along with the fact that the importance of incorporating such details in Semantic Parsing formalisms has already been stressed in the literature (Donatelli et al., 2018; Bonial et al., 2019), leads us to implement these features to further enhance the representative power of BMR. To this end, we employ SpaCy in order to retrieve the Penn Treebank part-of-speech tags (Marcus et al., 1993), which inherently provide information with respect to number, tense, and aspect, for all the words and multiword expressions aligned with a node in the graphs. In practice, we account for tense by enriching each verbal node with the semantic role :timing showing a value of + or  to indicate events that will take place in the future or that happened in the past, respectively. Similarly, we handle plurality of the nominal nodes by adding the :quantity relation followed by a + value (see Figure 2). Lastly, we account for aspect by adding the relation :ongoing followed by a + mark to verbal nodes expressing the imperfective aspect (ongoing or usual actions). An interlingual representation of meaning has the basic requirement of being fully linked to an inventory of meanings which can be expressed in multiple languages. For this reason, in order to make nodes in BMR graphs language-independent, we enhance them with BabelNet synsets information. An example of why this is needed is provided in Figure 1, where the predicate take-01 employed in AMR is defined in OntoNotes with the very coarse-grained gloss of take, acquire, come to have, choose, bring with you from somewhere, receiving, internalizing, bringing along, enacting, and the ambiguous word plane is merely represented as a lexical node, which provides no cues for understanding whether it refers to an airplane, a geometric plane, or a carpenter's plane, inter alia. Moreover, the combination of the two does not clarify whether take the plane means to take a flight or to take the carpenter's plane somewhere. Lacking a pointer to a more fine-grained and multilingual word sense inventory also has the disadvantage of preventing the use of the formalism as a means of moving across languages effectively. For example, if the word parents is not assigned the proper word sense, it would lead to ambiguous translations in languages such as Spanish, where the corresponding word padres can indicate both the meaning of parents, but also the meaning of fathers. Therefore, the advantages that come from the disambiguation of nodes with BabelNet are twofold: (i) resolving language ambiguity while representing word meaning explicitly, and (ii) interconnecting the same meanings across languages. Adding the disambiguation information to AMR 3.0 graphs is our last step in order to complete its conversion to BMR 1.0. To this end, we employ a set of different strategies: (a) we exploit the mapping from VerbAtlas frames to BabelNet synsets to assign word senses to nodes based on their lemmas, (b), we use the Wikipedia page information featured in AMR nodes representing named entities to retrieve the corresponding synset BabelNet identifies that page with, and (c), we make use of ESCHER (Barba et al., 2021), 9 a state-of-the-art system for Word Sense Disambiguation, i.e., the task of automatically assigning a meaning to a word in context (Bevilacqua et al., 2021b), to disambiguate the nodes without word senses. As a result, we succeed in assigning a BabelNet synset to an overall figure of 92% AMR content nodes (i.e., nodes aligned with content words), with 42 , 549 fully disambiguated graphs out of 59 , 255 . To demonstrate the importance of BMR's semantic framing, its aptness at preserving lexical information, and its effectiveness in acting as an interlingual representation, we devise three experiments to assess its performance in comparison with AMR. Before delving into their details (Section 5.2), as well as describing our models and the evaluation measures we employ (Sections 5.3 and 5.4, respec-tively), we first provide thorough information regarding the datasets used in our experiments. Aside from the original AMR 3.0 and BMR 1.0 datasets described in Section 4, 10 the following datasets are employed in our experiments, namely: (i) AMR + , which features the set of enhancements applied to the English AMR 3.0, as described from Section 4.1 to 4.3 (excluding node disambiguation), and (ii) BMR*, i.e., a version of BMR 1.0 that does not include lemma information. For each dataset, we also create language-specific versions in German (DE), Italian (IT) and Spanish (ES): starting from the English AMR 3.0, we followed Blloshmi et al. (2020) and create training and development sets for these languages by using gold AMR graphs  and their converted AMR + , BMR and BMR* versions  and pairing them with silver sentences translated with the machine translation models of Tiedemann and Thottingal (2020, OPUS-MT). As test data, we use the 1 , 371 parallel sentences of Abstract Meaning Representation 2.0 Four Translations, 11 that translate into our set of non-English languages their English (EN) counterparts (a subset of AMR 3.0) found in the AMR 2.0 test split. 12 5.2 Tasks Graph-to-Text (GtoT) Our first experiment concerns the Graph-to-Text generation task, i.e., the task of transforming graph meaning representations into their corresponding text, and has the goal of appraising the effectiveness of BMR as a tool for generating texts in different languages. In this context, we also conduct an ablation study on AMR + 10 We use the training/development/test splits of AMR 3.0 for both AMR 3.0 and BMR 1.0 datasets. Text-to-Graph (TtoG) Our second experiment deals instead with the Text-to-Graph generation task (Semantic Parsing), i.e., the task of generating a graph according to a given formalism, starting from raw text. The aim of TtoG is to assess the complexity of generating BMR graphs compared to AMR ones. Text-to-Graph-to-Text (TGT) Finally, in the third experiment, we evaluate the suitability of AMR and BMR to be used as interlingual representations by means of the combination of Text-to-Graph and Graph-to-Text parsing going from a source to a target language. In the same context, we also conduct an ablation study on BMR to assess the impact of the disambiguation in the graphs. All models employed in our experiments are built on top of SPRING (Bevilacqua et al., 2021a), an auto-regressive model for AMR parsing and generation based on the BART (Lewis et al., 2020) pretrained language model. Since the original SPRING works with pairs of sentences and linearized versions of the graphs, we modify its tok-enizer to account for BMR nodes, since they contain BabelNet synset IDs too. Furthermore, we add all synsets that appear more than once 13 within BMR 1.0 to the model's vocabulary and adapt SPRING to the mBART language model (Liu et al., 2020) in order to account for multiple languages in the GtoT and TGT experiments. Given the datasets described in Section 5.1, we confront models trained on AMR 3.0, BMR 1.0, AMR + and BMR* for each language (AMR/BMR/AMR + /BMR* EN,DE,IT,ES ). As regards the ablation study of the GtoT experiment, we apply each modification introduced to AMR 3.0 one at a time, and obtain several versions of the dataset, each of which is used to train additional models, namely, AMR 3.0 (i) including self-explanatory relations (AMRREL ), (ii) including self-explanatory relations and node merging (AMRNOD ), (iii) featuring the number category (AMRNUM ), (iv) featuring the tense and aspect categories (AMRTEN ), and (v) featuring the number, tense and aspect categories together (AMRNT ). Language English (EN) German (DE) Italian (IT) Spanish (ES) Model AMR AMR + BMR BMR  AMR AMR + BMR BMR  AMR AMR + BMR BMR  AMR AMR + BMR BMR  BLEU 44.8 49.8 50.7 45.7 23.2 24.3 24.8 22.2 29.0 31.3 31.4 29.1 34.6 36.8 37.3 35.5 chrF++ 73.4 76.0 76.3 72.1 55.8 57.0 57.1 54.7 60.7 62.1 62.2 60.0 64.0 65.2 65.5 63.7 METEOR 42.2 43.9 44.3 42.4 25.4 26.4 26.4 25.3 28.9 30.4 30.5 29.2 32.4 33.5 33.7 32.8 Rouge-L 68.2 71.7 72.8 69.7 49.3 50.7 51.1 49.7 51.9 54.2 54.3 52.4 57.4 60.9 61.0 59.8 To evaluate the text generation tasks (i.e., GtoT and TGT), we use five standard Natural Language Generation measures, namely, BLEU (Papineni et al., 2002), chrF++ (Popovic, 2017), METEOR (Banerjee and Lavie, 2005), and ROUGE-L (Lin, 2004), tokenizing system predictions with the JAMR script (Flanigan et al., 2014). For the TtoG experiment, instead, as is customary, we employ the Smatch measure (Cai and Knight, 2013). Results for the GtoT experiment are reported in Table 1. As can be seen, BMR obtains the highest scores for all the measures across the board, testifying to its effectiveness at generating text in multiple languages. Interestingly, when BMR is confronted with AMR + , the benefits of featuring disambiguation information immediately become evident, with highest scores on each measure. Results for the ablation study are, instead, shown in Table 2. Even though the impact of self-explanatory relations is not striking in this scenario (AMRREL model), the use of node merging already leads to an evident performance boost, particularly for BLEU and ROUGE-L (AMRNOD ). Not surprisingly, the addition of the grammatical categories of number, tense, and aspect to AMR 3.0 corroborates the thesis of Donatelli et al. (2018) and Bonial et al. (2019), with results for the different measures growing between 1 . 3 to 4 . 2 points for AMRNT compared to the baseline AMREN model. Moreover, demostrating the beneficial interaction of all features described in Section 4, the AMR + EN model significantly outperforms the baseline model by 1 . 7 points on METEOR (low-est) and 5 . 0 points on BLEU (highest), while also outscoring each other model featuring only specific modifications. Results for this experiment are shown in Table 3 and provide evidence for the high degree of complexity that BMR graphs have in comparison to their AMR counterparts. In particular, AMR + EN (which, except for the disambiguated nodes, has the same graph structure as BMREN ) outperforms BMREN by 3 . 5 Smatch points, demostrating that the extra layer represented by the inclusion of disambiguation information makes BMR graphs harder to generate automatically starting from raw text. As a matter of fact, a model attempting to generate BMR graphs needs to provide disambiguation for each node (and not just for the verbal predi-cates), hence it faces a much more difficult task. Finally, in Table 4 we report the scores for the TGT experiment, by means of which we appraise the capability of formalisms to act as bridges to translate sentences, first, performing a Text-to-Graph step, and then a Graph-to-Text one. Despite having shown lower performances in comparison to AMR 1733 Pairs EN  EN EN  DE EN  IT EN  ES Model AMR AMR + BMR BMR  AMR AMR + BMR BMR  AMR AMR + BMR BMR  AMR AMR + BMR BMR  BLEU 45.3 49.3 50.1 45.1 23.0 25.1 24.4 22.8 29.0 30.7 30.9 29.1 34.0 36.5 36.6 35.4 chrF++ 73.5 75.2 75.4 71.4 55.6 56.8 56.1 54.1 60.2 61.4 61.2 59.8 63.3 64.6 64.8 63.3 METEOR 42.3 43.5 43.7 41.9 25.4 26.4 26.0 25.1 28.7 29.8 29.9 29.1 32.0 33.3 33.2 32.6 Rouge-L 68.8 71.8 73.0 69.5 49.6 50.8 50.8 49.3 51.9 53.5 53.2 52.6 57.2 61.1 62.2 59.7 Table 4: Results for the TGT experiment. in the TtoG experiment, the high scores obtained by BMR in this experiment demonstrate that it is better suited as an interlingua. Nevertheless, AMR + outperforms BMR in a few settings, likely due to the higher complexity entailed by BMR parsing, as explained in Section 6.2. Corroborating this thesis are the results shown in Table 5, where BMR scores are compared against a model (AMR+*) in which, to perform the Graph-to-Text step, AMR+ uses a BMR parser with the synset information removed, rather than its own parser. The outcome of this ablation study, with BMR now systematically outscoring its competitor, sheds further light upon the effectiveness of synset-driven disambiguation for encoding valuable sentence information. Returning to the results given in Table 4, even though performances for BMR* models are the lowest (yet competitive, and sometimes higher than AMR) on the board, it is worth remarking that this setting does not feature the lemma information. In fact, in order to be purely semantic, BMR graphs should solely feature the BabelNet synset information. However, given that state-of-the-art Semantic Parsing and generation models make use of pre-trained language models such as BART and mBART, which are trained with data in human language (hence devoid of synset information), the performance of fully-semantic models drops if lemmas are not taken into account. Additionally, currently available text generation metrics are suboptimal when employed to assess semantics, since these measures evaluate similarities at the lemma level. Therefore, though a fully-semantic model could infer the meaning of a BabelNet synset, its performances will be penalized for not generating specific lemmas while outputting perfectly suitable synonyms. In view of this, BMR 1.0 incorporates the lemma information along with the BabelNet synset specifying its meaning (see also Appendix A), demostrating that lexical-semantic representations improve over purely lexical ones. Results for the experiments we conducted depict BMR* as the model that, on the whole, achieves the lowest scores. With the aim of showing how such results might arise due to inadequate evaluation measures (see Section 6.3), we propose a focused case study in which we qualitatively inspect the differences between graphs and sentences generated by means of the AMR and BMR* models. Starting from the sentence My friend did not tolerate his father's behaviour (Figure 3), it can be seen how the grammatical categories of number and tense for the words friend and tolerate are correctly preserved by BMR* only. Additionally, it can be noted how the complex structure that defines child in AMR can confuse the model when there is a reentrant node (in this case, the model does not know to whom the father is related). As interestingly, the sentence generated via BMR* replaces tolerate with the synonym put up with , which worsens its performance according to exact string matching metrics, but, at the same time, provides an insight of a higher level of abstraction when lemmas are omitted. Although the experiments reported in Section 5 testify to the quality of BMR, following an in-house behavioral analysis inspired by the work of Ribeiro et al. (2020), we identify three main classes of errors that undermine the application of BMR as an interlingua, one concerning the formalism ( repository contraints ), one tied to the data contained in the BMR 1.0 dataset ( disambiguation constraints ), Repository constraints BabelNet features a wealth of synsets covering content words in a multilingual setting, but, at the same time, does not provide information regarding parts of speech other than nouns, verbs, adjectives and adverbs. As a result, BMR uses language-specific lemmas to represent conjunctions or ambiguous pronouns such as anyone , which can mean either not a single person or everyone, depending on the use of negative or positive phrasing. On a different note, with roughly 6,500 languages spoken in the world and BabelNet 5.0 featuring a subset of them, the definition of BMR as an interlingua is actually constrained to the number, albeit large, of 500 BabelNet languages. Disambiguation constraints The creation of BMR 1.0 is based upon the Word Sense Disambiguation task carried out via a state-of-the-art system (Barba et al., 2021, ESCHER). And yet, this neural architecture is trained to predict word senses featured in the WordNet 3.0 sense inventory only. By virtue of the fact that, following the node merging strategy (Section 4.2), we can obtain polysemous multiwords found in BabelNet but not in WordNet (as is the case of run off at the mouth ), we cannot provide disambiguation for such instances. This justifies the fact that 8% of content nodes in BMR are not disambiguated (see also Section 4.4). Language-specific constraints The number of items in a lexicon and the degree of word polysemy vary from language to language (Talmy, 2000). Using BabelNet synsets to represent abstract concepts and connect them multilingually is certainly a desirable feature. However, there are concepts and expressions that exist in a given language only, e.g., owing to their being culturally connoted. For example, the Spanish word espeto , which refers to a traditional way of cooking freshly-caught sea fish, has no equivalent in English. Though the concept is featured in BabelNet, it has no lexicalizations in other languages and, as such, it would need to be paraphrased in order to be rendered. Current Semantic Parsing formalisms share tight dependencies with semantic repositories which are both language-specific and isolated from word senses in other languages. As a result, they are not fit to be used as interlingual representations of Figure 3: AMR and BMR* graph representations and generated sentences for the original sentence My friend did not tolerate his father's behaviour . meaning. In this paper, we put forward BMR, a new language-independent formalism that abstracts away from language-specific constraints thanks to two multilingual semantic resources, namely, BabelNet and VerbAtlas. To put our formalism into practice, we also created BMR 1.0, the first dataset labeled according to BMR. Our experiments demostrate the impact that the fully-semantic framing of our formalism has in comparison to the widely-employed formalism of AMR, as well as showing its ability to be a better tool at encoding textual information, and a much more effective interlingua in a text-to-graph-to-text machine translation task. As future work, we plan to (i) create a single multilingual model to parse graphs and generate text in any language, (ii) apply BMR cross-lingually to other downstream tasks such as text summarization, (iii) evolve the formalism to prevent the inclusion of lexical information of any kind. We make our code and data available to the research community at https://github. com/SapienzaNLP/bmr . The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487, the Marie Skodowska-Curie project Knowledge Graphs at Scale (Know-Graphs) No. 860801, and the ELEXIS project No. 731015 under the European Union's Horizon 2020 research and innovation programme.
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