Shortcut-Stacked Sentence Encoders for Multi-Domain Inference
Yixin Nie, Mohit Bansal · 2017
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 singlemodel result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)).Moreover, they achieve the new state-of-theart encoding result on the original SNLI dataset (Bowman et al., 2015).