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.
View on arXiv@article{josé2025_2407.04858,
title={ Question Answering with Texts and Tables through Deep Reinforcement Learning },
author={ Marcos M. José and Flávio N. Cação and Maria F. Ribeiro and Rafael M. Cheang and Paulo Pirozelli and Fabio G. Cozman },
journal={arXiv preprint arXiv:2407.04858},
year={ 2025 }
}