Question Answering with Texts and Tables through Deep Reinforcement Learning
<|reference_start|>Question Answering with Texts and Tables through Deep Reinforcement Learning: 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.<|reference_end|>
@article{josé2024question,
    title={Question Answering with Texts and Tables through Deep Reinforcement
  Learning},
    author={Marcos M. Jos'e, Fl'avio N. Cac{c}~ao, Maria F. Ribeiro, Rafael M.
  Cheang, Paulo Pirozelli, Fabio G. Cozman},
    journal={arXiv preprint arXiv:2407.04858},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04858},
    primaryClass={cs.CL}
}