@inproceedings{das-etal-2021-case,
title = "Case-based Reasoning for Natural Language Queries over Knowledge Bases",
author = "Das, Rajarshi and
Zaheer, Manzil and
Thai, Dung and
Godbole, Ameya and
Perez, Ethan and
Lee, Jay Yoon and
Tan, Lizhen and
Polymenakos, Lazaros and
McCallum, Andrew",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.755",
doi = "10.18653/v1/2021.emnlp-main.755",
pages = "9594--9611",
abstract = "It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions {---} a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11{\%} on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases \textit{without} any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.",
}
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<abstract>It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions — a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.</abstract>
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%0 Conference Proceedings
%T Case-based Reasoning for Natural Language Queries over Knowledge Bases
%A Das, Rajarshi
%A Zaheer, Manzil
%A Thai, Dung
%A Godbole, Ameya
%A Perez, Ethan
%A Lee, Jay Yoon
%A Tan, Lizhen
%A Polymenakos, Lazaros
%A McCallum, Andrew
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F das-etal-2021-case
%X It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions — a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.
%R 10.18653/v1/2021.emnlp-main.755
%U https://aclanthology.org/2021.emnlp-main.755
%U https://doi.org/10.18653/v1/2021.emnlp-main.755
%P 9594-9611
Markdown (Informal)
[Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://aclanthology.org/2021.emnlp-main.755) (Das et al., EMNLP 2021)
ACL
- Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, and Andrew McCallum. 2021. Case-based Reasoning for Natural Language Queries over Knowledge Bases. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9594–9611, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.