| Texto completo | |
| Autor(es): |
Jose, Marcos M.
;
Cacao, Flavio N.
;
Ribeiro, Maria F.
;
Cheang, Rafael M.
;
Pirozelli, Paulo
;
Cozman, Fabio G.
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTELLIGENT SYSTEMS, BRACIS 2024, PT II; v. 15413, p. 15-pg., 2025-01-01. |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia |
| Processo FAPESP: | 19/26762-0 - Estruturas Lógicas em Argumentação |
| Beneficiário: | Paulo Pirozelli Almeida Silva |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |