Timothy O'Donnell

Dzmitry Bahdanau

Compositionality— the ability to combine familiar units like words into novel phrases and sentences— has been the focus of intense inter… (voir plus)est in artificial intelligence in recent years. To test compositional generalization in semantic parsing, Keysers et al. (2020) introduced Compositional Freebase Queries (CFQ). This dataset maximizes the similarity between the test and train distributions over primitive units, like words, while maximizing the compound divergence: the dissimilarity between test and train distributions over larger structures, like phrases. Dependency parsing, however, lacks a compositional generalization benchmark. In this work, we introduce a gold-standard set of dependency parses for CFQ, and use this to analyze the behaviour of a state-of-the art dependency parser (Qi et al., 2020) on the CFQ dataset. We find that increasing compound divergence degrades dependency parsing performance, although not as dramatically as semantic parsing performance. Additionally, we find the performance of the dependency parser does not uniformly degrade relative to compound divergence, and the parser performs differently on different splits with the same compound divergence. We explore a number of hypotheses for what causes the non-uniform degradation in dependency parsing performance, and identify a number of syntactic structures that drive the dependency parser’s lower performance on the most challenging splits.

2021-10-13

ArXiv (preprint)

Characterizing Idioms: Conventionality and Contingency

Michaela Socolof

Jackie Cheung

Michael Wagner

Idioms are unlike most phrases in two important ways. First, words in an idiom have non-canonical meanings. Second, the non-canonical meanin… (voir plus)gs of words in an idiom are contingent on the presence of other words in the idiom. Linguistic theories differ on whether these properties depend on one another, as well as whether special theoretical machinery is needed to accommodate idioms. We define two measures that correspond to the properties above, and we show that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. Our results suggest that introducing special machinery to handle idioms may not be warranted.

2021-04-17

ArXiv (preprint)

Recursive Top-Down Production for Sentence Generation with Latent Trees

Shawn Tan

Yikang Shen

Alessandro Sordoni

Aaron Courville

2020-11-01

Findings of the Association for Computational Linguistics: EMNLP 2020 (publié)

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach

Wenyu Du

Zhouhan Lin

Yikang Shen

Yoshua Bengio

Yue Sara Zhang

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally eff… (voir plus)iciently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called “syntactic distances”, where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality.

2020-07-01

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (publié)

CLOSURE: Assessing Systematic Generalization of CLEVR Models

Dzmitry Bahdanau

Harm de Vries