Sentence Embeddings in NLI with Iterative Refinement Encoders

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http://hdl.handle.net/10138/304483

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Talman , A J , Yli-Jyrä , A & Tiedemann , J 2019 , ' Sentence Embeddings in NLI with Iterative Refinement Encoders ' , Natural Language Engineering , vol. 25 , no. 4 , pp. 467-482 . https://doi.org/10.1017/s1351324919000202

Titel: Sentence Embeddings in NLI with Iterative Refinement Encoders
Författare: Talman, Aarne Johannes; Yli-Jyrä, Anssi; Tiedemann, Jörg
Medarbetare: University of Helsinki, Department of Digital Humanities
University of Helsinki, Language Technology
University of Helsinki, Department of Digital Humanities
Datum: 2019-07-31
Språk: eng
Sidantal: 16
Tillhör serie: Natural Language Engineering
Permanenta länken (URI): http://hdl.handle.net/10138/304483
Abstrakt: Sentence-level representations are necessary for various NLP tasks. Recurrent neural networks have proven to be very effective in learning distributed representations and can be trained efficiently on natural language inference tasks. We build on top of one such model and propose a hierarchy of BiLSTM and max pooling layers that implements an iterative refinement strategy and yields state of the art results on the SciTail dataset as well as strong results for SNLI and MultiNLI. We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks. Furthermore, our model beats the InferSent model in 8 out of 10 recently published SentEval probing tasks designed to evaluate sentence embeddings' ability to capture some of the important linguistic properties of sentences.
Subject: 113 Computer and information sciences
6121 Languages
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