Large-scale Simple Question Answering with Memory Networks

5 Jun 2015Antoine Bordes • Nicolas Usunier • Sumit Chopra • Jason Weston

Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct evidence given a question, which can be difficult in large-scale conditions. To this end, we introduce a new dataset of 100k questions that we use in conjunction with existing benchmarks.

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Task Dataset Model Metric name Metric value Global rank Compare
Question Answering Reverb Memory Networks (ensemble) Accuracy 68% # 2
Question Answering SimpleQuestions Memory Networks (ensemble) F1 63.9% # 1
Question Answering WebQuestions Memory Networks (ensemble) F1 42.2% # 1