Question Answering with Texts and Tables through Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2407.04858v1
• Date: Fri, 5 Jul 2024 20:44:01 GMT
• Title: Question Answering with Texts and Tables through Deep Reinforcement Learning
• Authors: Marcos M. José, Flávio N. Cação, Maria F. Ribeiro, Rafael M. Cheang, Paulo Pirozelli, Fabio G. Cozman,
• Abstract summary: This paper proposes a novel architecture to generate multi-hop answers to open domain questions that require information from texts and tables. Our architecture employs reinforcement learning to choose between different state-of-the-art tools sequentially until, in the end, a desired answer is generated. This system achieved an F1-score of 19.03, comparable to iterative systems in the literature.
• Score: 0.06597195879147556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a novel architecture to generate multi-hop answers to open domain questions that require information from texts and tables, using the Open Table-and-Text Question Answering dataset for validation and training. One of the most common ways to generate answers in this setting is to retrieve information sequentially, where a selected piece of data helps searching for the next piece. As different models can have distinct behaviors when called in this sequential information search, a challenge is how to select models at each step. Our architecture employs reinforcement learning to choose between different state-of-the-art tools sequentially until, in the end, a desired answer is generated. This system achieved an F1-score of 19.03, comparable to iterative systems in the literature.

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