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            <namePart type="given">Samuel</namePart>
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    <abstract>We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top single-model result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-the-art encoding result on the original SNLI dataset (Bowman et al., 2015).</abstract>
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%0 Conference Proceedings
%T Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
%A Nie, Yixin
%A Bansal, Mohit
%Y Bowman, Samuel
%Y Goldberg, Yoav
%Y Hill, Felix
%Y Lazaridou, Angeliki
%Y Levy, Omer
%Y Reichart, Roi
%Y Søgaard, Anders
%S Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F nie-bansal-2017-shortcut
%X We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top single-model result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-the-art encoding result on the original SNLI dataset (Bowman et al., 2015).
%R 10.18653/v1/W17-5308
%U https://aclanthology.org/W17-5308
%U https://doi.org/10.18653/v1/W17-5308
%P 41-45

Markdown (Informal)

[Shortcut-Stacked Sentence Encoders for Multi-Domain Inference](https://aclanthology.org/W17-5308) (Nie & Bansal, RepEval 2017)

• Shortcut-Stacked Sentence Encoders for Multi-Domain Inference (Nie & Bansal, RepEval 2017)

ACL

• Yixin Nie and Mohit Bansal. 2017. Shortcut-Stacked Sentence Encoders for Multi-Domain Inference. In Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP, pages 41–45, Copenhagen, Denmark. Association for Computational Linguistics.