Query-Reduction Networks for Question Answering

14 Jun 2016Minjoon Seo • Sewon Min • Ali Farhadi • Hannaneh Hajishirzi

In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Question Answering bAbi QRN Accuracy (trained on 10k) 99.7% # 1
Question Answering bAbi QRN Accuracy (trained on 1k) 90.1% # 1
Question Answering bAbi QRN Mean Error Rate 0.3% # 1