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.
|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|