Contrasting Distinct Structured Views to Learn Sentence Embeddings

TitleContrasting Distinct Structured Views to Learn Sentence Embeddings
Publication TypeArticle dans des actes
Année de la conférence2021
AuthorsSimoulin, Antoine, and Benoît Crabbé
Nom de la conférenceProceedings of the 16th {Conference of the {European Chapter of the {Association for {Computational Linguistics: {Student Research Workshop
PublisherAssociation for Computational Linguistics
Conference LocationOnline

We propose a self-supervised method that builds sentence embeddings from the combination of diverse explicit syntactic structures of a sentence. We assume structure is crucial to building consistent representations as we expect sentence meaning to be a function of both syntax and semantic aspects. In this perspective, we hypothesize that some linguistic representations might be better adapted given the considered task or sentence. We, therefore, propose to learn individual representation functions for different syntactic frameworks jointly. Again, by hypothesis, all such functions should encode similar semantic information differently and consequently, be complementary for building better sentential semantic embeddings. To assess such hypothesis, we propose an original contrastive multi-view framework that induces an explicit interaction between models during the training phase. We make experiments combining various structures such as dependency, constituency, or sequential schemes. Our results outperform comparable methods on several tasks from standard sentence embedding benchmarks.