@inproceedings{kurosawa-yanaka-2022-logical,
title = "Logical Inference for Counting on Semi-structured Tables",
author = "Kurosawa, Tomoya and
Yanaka, Hitomi",
editor = "Louvan, Samuel and
Madotto, Andrea and
Madureira, Brielen",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.8",
doi = "10.18653/v1/2022.acl-srw.8",
pages = "84--96",
abstract = "Recently, the Natural Language Inference (NLI) task has been studied for semi-structured tables that do not have a strict format. Although neural approaches have achieved high performance in various types of NLI, including NLI between semi-structured tables and texts, they still have difficulty in performing a numerical type of inference, such as counting. To handle a numerical type of inference, we propose a logical inference system for reasoning between semi-structured tables and texts. We use logical representations as meaning representations for tables and texts and use model checking to handle a numerical type of inference between texts and tables. To evaluate the extent to which our system can perform inference with numerical comparatives, we make an evaluation protocol that focuses on numerical understanding between semi-structured tables and texts in English. We show that our system can more robustly perform inference between tables and texts that requires numerical understanding compared with current neural approaches.",
}
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%0 Conference Proceedings
%T Logical Inference for Counting on Semi-structured Tables
%A Kurosawa, Tomoya
%A Yanaka, Hitomi
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kurosawa-yanaka-2022-logical
%X Recently, the Natural Language Inference (NLI) task has been studied for semi-structured tables that do not have a strict format. Although neural approaches have achieved high performance in various types of NLI, including NLI between semi-structured tables and texts, they still have difficulty in performing a numerical type of inference, such as counting. To handle a numerical type of inference, we propose a logical inference system for reasoning between semi-structured tables and texts. We use logical representations as meaning representations for tables and texts and use model checking to handle a numerical type of inference between texts and tables. To evaluate the extent to which our system can perform inference with numerical comparatives, we make an evaluation protocol that focuses on numerical understanding between semi-structured tables and texts in English. We show that our system can more robustly perform inference between tables and texts that requires numerical understanding compared with current neural approaches.
%R 10.18653/v1/2022.acl-srw.8
%U https://aclanthology.org/2022.acl-srw.8
%U https://doi.org/10.18653/v1/2022.acl-srw.8
%P 84-96
Markdown (Informal)
[Logical Inference for Counting on Semi-structured Tables](https://aclanthology.org/2022.acl-srw.8) (Kurosawa & Yanaka, ACL 2022)
ACL
- Tomoya Kurosawa and Hitomi Yanaka. 2022. Logical Inference for Counting on Semi-structured Tables. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 84–96, Dublin, Ireland. Association for Computational Linguistics.