Discrete Reasoning Templates for Natural Language Understanding

Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo


Abstract
Reasoning about information from multiple parts of a passage to derive an answer is an open challenge for reading-comprehension models. In this paper, we present an approach that reasons about complex questions by decomposing them to simpler subquestions that can take advantage of single-span extraction reading-comprehension models, and derives the final answer according to instructions in a predefined reasoning template. We focus on subtraction based arithmetic questions and evaluate our approach on a subset of the DROP dataset. We show that our approach is competitive with the state of the art while being interpretable and requires little supervision.
Anthology ID:
2021.eacl-srw.12
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
April
Year:
2021
Address:
Online
Editors:
Ionut-Teodor Sorodoc, Madhumita Sushil, Ece Takmaz, Eneko Agirre
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
80–87
Language:
URL:
https://aclanthology.org/2021.eacl-srw.12
DOI:
10.18653/v1/2021.eacl-srw.12
Bibkey:
Cite (ACL):
Hadeel Al-Negheimish, Pranava Madhyastha, and Alessandra Russo. 2021. Discrete Reasoning Templates for Natural Language Understanding. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 80–87, Online. Association for Computational Linguistics.
Cite (Informal):
Discrete Reasoning Templates for Natural Language Understanding (Al-Negheimish et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-srw.12.pdf
Data
DROPSQuAD