Contrasting distinct structured views to learn sentence embeddings

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 build consistent representations as we expect sentence meaning to be a function from 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 jointly learn individual representation functions for different syntactic frameworks. 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. We evaluate our method on standard sentence embedding benchmarks. Our results outperform comparable methods on several tasks.