Tensor-Based Preposition Representation
Prepositions are among the most frequent words. Good prepositional representation is of great syntactic and semantic interest in computational linguistics. Existing methods on preposition representation either treat prepositions as content words (e.g., word2vec and GloVe) or depend heavily on external linguistic resources including syntactic parsing, training task and dataset-specific representations. In this paper we use word-triple counts (one of the words is a preposition) to capture the preposition*s interaction with its head and children. Prepositional embeddings are derived via tensor decompositions on a large unlabeled corpus. We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing of phrasal verbs. Furthermore, our prepositional embeddings are used as simple features to two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve comparable to or better results than state of the art on multiple standardized datasets.
 This paper proposes to learn vector representations of prepositions by learning them as tensor decompositions of a triple of a left word (maybe head), the preposition, and right word (maybe dependent). This is an interesting idea with linguistic validity, and practically possible because of the commonness and promiscuity of prepositions, reflecting their primary grammatical and relational roles (as function words not content words). The resulting representations are show to be useful – they produce SOTA results on preposition selection by a decent margin (on a practically useful and recently studied tasks that is arguably the task that best reflects on the quality of the learned representations) and good (but not quite SOTA) results, without further linguistic features beyond POS tags, on the much more studied task of preposition attachment disambiguation. Overall, I really liked this paper. Despite this enthusiasm, I am doubtful whether this is a good paper for ICLR. And I write this as a card-carrying computational linguist. This is partly because of the writing. It is very hard not to see the content of the introduction as addressing a linguistics/computational linguistics audience rather than the mainstream of the ICLR audience (you get this impression rather strongly from the start of each of the first 3 paragraphs of the introduction...). More profoundly, this impression comes from the nature of the investigation and results. While this paper makes a contribution to representation learning in suggesting a good way to learn a representation for prepositions, it does not make any contributions to methods of representation learning. Indeed, it is basically an application of the orthogonalized alternating least squares method of Sharan and Valiant (2017) and more generally of the tensor decomposition ideas of numerous papers of Anandkumar and colleagues. There aren*t any new technical ideas here. While the learned representations are successful for the two main performance tasks discussed above, the ancillary evidence provided from the paraphrasing of prepositional phrase seems highly uncompelling to me. That is, the task seems a completely valid one – one would like to be able to show that "sparked off" is a synonym of "provoked", but the actually results provided on this task seem quite uncompelling. Among other things, the example used in the text in section 3 seems bad to me. It isn*t really the case that "split off something" means "divided something". ("Sally split off a sliver of wood" does not mean "Sally divided a sliver of wood". "separated" would be much closer. Indeed, of the examples in Table 2, the first 3 look bad, the fourth isn*t generally true but valid in certain contexts, the 5th is again wrong and only the 6th is really good. Similar remarks for the many more examples in the supplementary materials. The most intriguing question is the one raised in the first paragraph of the conclusion: While prepositions are natural for modeling via word triples and indeed their high frequency and small number of types makes this quite practical, the kind of concerns raised here are also applicable to a whole bunch of word types, and it would be natural to want to extend the method to them. E.g., we would also like to learn synonymy with light verb like "take note" or "pay attention" means roughly "notice" or "observe"; or the widely studied SVO triples like <rock,sank,ship> would also seem to cry out for a tensor decomposition. It would be interesting to think about what further might be done here. Minor comments: - Abstract: saying that word2vec and GloVe treat prepositions as "content words" seems slightly wrong; really they treat them just as "words" since all words are treated the same – though one can argue that most words are content words and the method of modeling word meaning is generally much more appropriate for content words. - p.2: "folklore within the NLP community". I*m not sure whether this is true or not; while pairwise counts have been the method of choice in recent word vector learning methods, it wasn*t true of older methods (Collobert and Weston or Bengio*s NPLM) and n-gram counts for n > 2 are widespread in pre-neural NLP. At any rate, it is rather unconvincing when the only evidence you cite is the paper by Sharan and Valiant, where AFAIK, neither author has ever published an NLP paper or attended an NLP conference.... - p.5 FEC should be FCE; Ng et al. (2014) should be (Ng et al., 2014)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Tensor-Based Preposition Representation
Prepositions are among the most frequent words. Good prepositional representation is of great syntactic and semantic interest in computational linguistics. Existing methods on preposition representation either treat prepositions as content words (e.g., word2vec and GloVe) or depend heavily on external linguistic resources including syntactic parsing, training task and dataset-specific representations. In this paper we use word-triple counts (one of the words is a preposition) to capture the preposition*s interaction with its head and children. Prepositional embeddings are derived via tensor decompositions on a large unlabeled corpus. We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing of phrasal verbs. Furthermore, our prepositional embeddings are used as simple features to two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve comparable to or better results than state of the art on multiple standardized datasets.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposes to learn vector representations of prepositions by learning them as tensor decompositions of a triple of a left word (maybe head), the preposition, and right word (maybe dependent). This is an interesting idea with linguistic validity, and practically possible because of the commonness and promiscuity of prepositions, reflecting their primary grammatical and relational roles (as function words not content words). The resulting representations are show to be useful – they produce SOTA results on preposition selection by a decent margin (on a practically useful and recently studied tasks that is arguably the task that best reflects on the quality of the learned representations) and good (but not quite SOTA) results, without further linguistic features beyond POS tags, on the much more studied task of preposition attachment disambiguation. Overall, I really liked this paper. Despite this enthusiasm, I am doubtful whether this is a good paper for ICLR. And I write this as a card-carrying computational linguist. This is partly because of the writing. It is very hard not to see the content of the introduction as addressing a linguistics/computational linguistics audience rather than the mainstream of the ICLR audience (you get this impression rather strongly from the start of each of the first 3 paragraphs of the introduction...). More profoundly, this impression comes from the nature of the investigation and results. While this paper makes a contribution to representation learning in suggesting a good way to learn a representation for prepositions, it does not make any contributions to methods of representation learning. Indeed, it is basically an application of the orthogonalized alternating least squares method of Sharan and Valiant (2017) and more generally of the tensor decomposition ideas of numerous papers of Anandkumar and colleagues. There aren*t any new technical ideas here. While the learned representations are successful for the two main performance tasks discussed above, the ancillary evidence provided from the paraphrasing of prepositional phrase seems highly uncompelling to me. That is, the task seems a completely valid one – one would like to be able to show that "sparked off" is a synonym of "provoked", but the actually results provided on this task seem quite uncompelling. Among other things, the example used in the text in section 3 seems bad to me. It isn*t really the case that "split off something" means "divided something". ("Sally split off a sliver of wood" does not mean "Sally divided a sliver of wood". "separated" would be much closer. Indeed, of the examples in Table 2, the first 3 look bad, the fourth isn*t generally true but valid in certain contexts, the 5th is again wrong and only the 6th is really good. Similar remarks for the many more examples in the supplementary materials. The most intriguing question is the one raised in the first paragraph of the conclusion: While prepositions are natural for modeling via word triples and indeed their high frequency and small number of types makes this quite practical, the kind of concerns raised here are also applicable to a whole bunch of word types, and it would be natural to want to extend the method to them. E.g., we would also like to learn synonymy with light verb like "take note" or "pay attention" means roughly "notice" or "observe"; or the widely studied SVO triples like <rock,sank,ship> would also seem to cry out for a tensor decomposition. It would be interesting to think about what further might be done here. Minor comments: - Abstract: saying that word2vec and GloVe treat prepositions as "content words" seems slightly wrong; really they treat them just as "words" since all words are treated the same – though one can argue that most words are content words and the method of modeling word meaning is generally much more appropriate for content words. - p.2: "folklore within the NLP community". I*m not sure whether this is true or not; while pairwise counts have been the method of choice in recent word vector learning methods, it wasn*t true of older methods (Collobert and Weston or Bengio*s NPLM) and n-gram counts for n > 2 are widespread in pre-neural NLP. At any rate, it is rather unconvincing when the only evidence you cite is the paper by Sharan and Valiant, where AFAIK, neither author has ever published an NLP paper or attended an NLP conference.... - p.5 FEC should be FCE; Ng et al. (2014) should be (Ng et al., 2014)
assistant
 This paper proposes to learn vector representations of prepositions by learning them as tensor decompositions of a triple of a left word (maybe head), the preposition, and right word (maybe dependent). This is an interesting idea with linguistic validity, and practically possible because of the commonness and promiscuity of prepositions, reflecting their primary grammatical and relational roles (as function words not content words). The resulting representations are show to be useful – they produce SOTA results on preposition selection by a decent margin (on a practically useful and recently studied tasks that is arguably the task that best reflects on the quality of the learned representations) and good (but not quite SOTA) results, without further linguistic features beyond POS tags, on the much more studied task of preposition attachment disambiguation. Overall, I really liked this paper. Despite this enthusiasm, I am doubtful whether this is a good paper for ICLR. And I write this as a card-carrying computational linguist. This is partly because of the writing. It is very hard not to see the content of the introduction as addressing a linguistics/computational linguistics audience rather than the mainstream of the ICLR audience (you get this impression rather strongly from the start of each of the first 3 paragraphs of the introduction...). More profoundly, this impression comes from the nature of the investigation and results. While this paper makes a contribution to representation learning in suggesting a good way to learn a representation for prepositions, it does not make any contributions to methods of representation learning. Indeed, it is basically an application of the orthogonalized alternating least squares method of Sharan and Valiant (2017) and more generally of the tensor decomposition ideas of numerous papers of Anandkumar and colleagues. There aren*t any new technical ideas here. While the learned representations are successful for the two main performance tasks discussed above, the ancillary evidence provided from the paraphrasing of prepositional phrase seems highly uncompelling to me. That is, the task seems a completely valid one – one would like to be able to show that "sparked off" is a synonym of "provoked", but the actually results provided on this task seem quite uncompelling. Among other things, the example used in the text in section 3 seems bad to me. It isn*t really the case that "split off something" means "divided something". ("Sally split off a sliver of wood" does not mean "Sally divided a sliver of wood". "separated" would be much closer. Indeed, of the examples in Table 2, the first 3 look bad, the fourth isn*t generally true but valid in certain contexts, the 5th is again wrong and only the 6th is really good. Similar remarks for the many more examples in the supplementary materials. The most intriguing question is the one raised in the first paragraph of the conclusion: While prepositions are natural for modeling via word triples and indeed their high frequency and small number of types makes this quite practical, the kind of concerns raised here are also applicable to a whole bunch of word types, and it would be natural to want to extend the method to them. E.g., we would also like to learn synonymy with light verb like "take note" or "pay attention" means roughly "notice" or "observe"; or the widely studied SVO triples like <rock,sank,ship> would also seem to cry out for a tensor decomposition. It would be interesting to think about what further might be done here. Minor comments: - Abstract: saying that word2vec and GloVe treat prepositions as "content words" seems slightly wrong; really they treat them just as "words" since all words are treated the same – though one can argue that most words are content words and the method of modeling word meaning is generally much more appropriate for content words. - p.2: "folklore within the NLP community". I*m not sure whether this is true or not; while pairwise counts have been the method of choice in recent word vector learning methods, it wasn*t true of older methods (Collobert and Weston or Bengio*s NPLM) and n-gram counts for n > 2 are widespread in pre-neural NLP. At any rate, it is rather unconvincing when the only evidence you cite is the paper by Sharan and Valiant, where AFAIK, neither author has ever published an NLP paper or attended an NLP conference.... - p.5 FEC should be FCE; Ng et al. (2014) should be (Ng et al., 2014)