Enhancing Arguments Recognition for Financial Mathematical Reasoning over Hybrid Data

Jinsu Lim, Yechan Hwang, Young-Jun Lee, Ho-Jin Choi

Abstract
Mathematical question answering over long-form documents is challenging across domains like finance or Wikipedia due to the abundance of candidate arguments within evidence, which complicates recognizing proper arguments for mathematical reasoning and poses hard to learning. In this paper, we propose an approach for training a generator to improve argument recognition. Our method enhances the probabilities of proper arguments in a reasoning program generation so that the arguments comprising the ground truth have higher weights. The proposed approach consists of an argument aggregator to model the probabilities in each candidate generation and an argument set loss to compute the cross-entropy between that probability and the candidates’ existence in the ground truth in terms of the argument set. In our experiments, we show performance improvements of 3.62% and 3.98% in execution accuracy and program accuracy, respectively, over the existing FinQANet model based on a financial mathematical QA dataset. Also, we observed that the similarity of argument sets between the generated program and the ground truth improved by about 2.9%, indicating a mitigation of the misrecognition problem.
Anthology ID:
2024.findings-emnlp.228
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3961–3973
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.228/
DOI:
10.18653/v1/2024.findings-emnlp.228
Bibkey:
Cite (ACL):
Jinsu Lim, Yechan Hwang, Young-Jun Lee, and Ho-Jin Choi. 2024. Enhancing Arguments Recognition for Financial Mathematical Reasoning over Hybrid Data. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3961–3973, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Enhancing Arguments Recognition for Financial Mathematical Reasoning over Hybrid Data (Lim et al., Findings 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.228.pdf
PDF Search