Structured Alignment Networks for Matching Sentences

Yang Liu, Matt Gardner, Maria Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically, this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence. When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors. We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures. Using a structured attention mechanism, our model matches candidate spans in the first sentence to candidate spans in the second sentence, simultaneously discovering the tree structure of each sentence. Our model is fully differentiable and trained only on the matching objective. We evaluate this model on two tasks, natural entailment detection and answer sentence selection, and find that modeling latent tree structures results in superior performance. Analysis of the learned sentence structures shows they can reflect some syntactic phenomena.
Original languageEnglish
Title of host publication2018 Conference on Empirical Methods in Natural Language Processing
Place of PublicationBrussels, Belgium
PublisherAssociation for Computational Linguistics
Number of pages11
Publication statusPublished - Nov 2018
Event2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018


Conference2018 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2018
Internet address


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