1. Question Answering with Texts and Tables through Deep Reinforcement Learning
- Author
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José, Marcos M., Cação, Flávio N., Ribeiro, Maria F., Cheang, Rafael M., Pirozelli, Paulo, and Cozman, Fabio G.
- Subjects
Computer Science - Computation and Language - 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.
- Published
- 2024