A Coarse to Fine Question Answering System based on Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based coarse-to-fine question answering system that efficiently handles documents of varying lengths, achieving higher accuracy and faster training than existing models across multiple datasets.

Contribution

The paper presents a novel multi-step QA system using actor-critic deep reinforcement learning that improves accuracy and training speed for both short and long documents.

Findings

Achieves 1.3%-1.7% accuracy improvements on four datasets.

Speeds up training by 1.5x to 3.4x compared to baselines.

Effectively handles documents of different lengths with a multi-step approach.

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.