Learning Natural Language Inference with LSTM

NAACL 2016 Shuohang WangJing Jiang

Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI)... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Natural Language Inference SNLI 300D mLSTM word-by-word attention model % Test Accuracy 86.1 # 28
Natural Language Inference SNLI 300D mLSTM word-by-word attention model % Train Accuracy 92.0 # 25
Natural Language Inference SNLI 300D mLSTM word-by-word attention model Parameters 1.9m # 1