@inproceedings{al-negheimish-etal-2021-discrete,
title = "Discrete Reasoning Templates for Natural Language Understanding",
author = "Al-Negheimish, Hadeel and
Madhyastha, Pranava and
Russo, Alessandra",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-srw.12",
doi = "10.18653/v1/2021.eacl-srw.12",
pages = "80--87",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Discrete Reasoning Templates for Natural Language Understanding
%A Al-Negheimish, Hadeel
%A Madhyastha, Pranava
%A Russo, Alessandra
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F al-negheimish-etal-2021-discrete
%X 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.
%R 10.18653/v1/2021.eacl-srw.12
%U https://aclanthology.org/2021.eacl-srw.12
%U https://doi.org/10.18653/v1/2021.eacl-srw.12
%P 80-87
Markdown (Informal)
[Discrete Reasoning Templates for Natural Language Understanding](https://aclanthology.org/2021.eacl-srw.12) (Al-Negheimish et al., EACL 2021)
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.