A Coarse to Fine Question Answering System based on Reinforcement Learning

• URL: http://arxiv.org/abs/2106.00257v1
• Date: Tue, 1 Jun 2021 06:41:48 GMT
• Title: A Coarse to Fine Question Answering System based on Reinforcement Learning
• Authors: Yu Wang, Hongxia Jin
• Abstract summary: The system is designed using an actor-critic based deep reinforcement learning model to achieve multi-step question answering. We test our model on four QA datasets, WIKEREADING, WIKIREADING LONG, CNN and SQuAD, and demonstrate 1.3$%$-1.7$%$ accuracy improvements with 1.5x-3.4x training speed-ups.
• Score: 48.80863342506432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a coarse to fine question answering (CFQA) system based on reinforcement learning which can efficiently processes documents with different lengths by choosing appropriate actions. The system is designed using an actor-critic based deep reinforcement learning model to achieve multi-step question answering. Compared to previous QA models targeting on datasets mainly containing either short or long documents, our multi-step coarse to fine model takes the merits from multiple system modules, which can handle both short and long documents. The system hence obtains a much better accuracy and faster trainings speed compared to the current state-of-the-art models. We test our model on four QA datasets, WIKEREADING, WIKIREADING LONG, CNN and SQuAD, and demonstrate 1.3$\%$-1.7$\%$ accuracy improvements with 1.5x-3.4x training speed-ups in comparison to the baselines using state-of-the-art models.

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