Shortcut-Stacked Sentence Encoders for Multi-Domain Inference

WS 2017  ·  Yixin Nie, Mohit Bansal ·

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 non-ensemble 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).

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Natural Language Inference SNLI 600D Residual stacked encoders % Test Accuracy 86.0 # 59
% Train Accuracy 91.0 # 39
Parameters 29m # 3
Natural Language Inference SNLI 300D Residual stacked encoders % Test Accuracy 85.7 # 64
% Train Accuracy 89.8 # 46
Parameters 9.7m # 3


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