@inproceedings{chen-etal-2022-convfinqa,
title = "{C}onv{F}in{QA}: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering",
author = "Chen, Zhiyu and
Li, Shiyang and
Smiley, Charese and
Ma, Zhiqiang and
Shah, Sameena and
Wang, William Yang",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.421",
doi = "10.18653/v1/2022.emnlp-main.421",
pages = "6279--6292",
abstract = "With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at \url{https://github.com/czyssrs/ConvFinQA}.",
}
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%0 Conference Proceedings
%T ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
%A Chen, Zhiyu
%A Li, Shiyang
%A Smiley, Charese
%A Ma, Zhiqiang
%A Shah, Sameena
%A Wang, William Yang
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-etal-2022-convfinqa
%X With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching. The community is experiencing the shift of the challenge from how to model language to the imitation of complex reasoning abilities like human beings. In this work, we investigate the application domain of finance that involves real-world, complex numerical reasoning. We propose a new large-scale dataset, ConvFinQA, aiming to study the chain of numerical reasoning in conversational question answering. Our dataset poses great challenge in modeling long-range, complex numerical reasoning paths in real-world conversations. We conduct comprehensive experiments and analyses with both the neural symbolic methods and the prompting-based methods, to provide insights into the reasoning mechanisms of these two divisions. We believe our new dataset should serve as a valuable resource to push forward the exploration of real-world, complex reasoning tasks as the next research focus. Our dataset and code is publicly available at https://github.com/czyssrs/ConvFinQA.
%R 10.18653/v1/2022.emnlp-main.421
%U https://aclanthology.org/2022.emnlp-main.421
%U https://doi.org/10.18653/v1/2022.emnlp-main.421
%P 6279-6292
Markdown (Informal)
[ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://aclanthology.org/2022.emnlp-main.421) (Chen et al., EMNLP 2022)
ACL