Gated Self-Matching Networks for Reading Comprehension and Question Answering

ACL 2017  ·  Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, Ming Zhou ·

In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3{\%} on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9{\%}. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Question Answering SQuAD1.1 dev R-NET (single) EM 71.1 # 35
F1 79.5 # 38

Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Question Answering SQuAD1.1 r-net (single model) EM 76.461 # 108
F1 84.265 # 113
Hardware Burden None # 1
Operations per network pass None # 1

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