@inproceedings{oguz-etal-2022-unik,
title = "{U}ni{K}-{QA}: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering",
author = "Oguz, Barlas and
Chen, Xilun and
Karpukhin, Vladimir and
Peshterliev, Stan and
Okhonko, Dmytro and
Schlichtkrull, Michael and
Gupta, Sonal and
Mehdad, Yashar and
Yih, Scott",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.115",
doi = "10.18653/v1/2022.findings-naacl.115",
pages = "1535--1546",
abstract = "We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retriever-reader model which has so far been limited to text sources only. Our approach greatly improves the results on knowledge-base QA tasks by 11 points, compared to latest graph-based methods. More importantly, we demonstrate that our unified knowledge (UniK-QA) model is a simple and yet effective way to combine heterogeneous sources of knowledge, advancing the state-of-the-art results on two popular question answering benchmarks, NaturalQuestions and WebQuestions, by 3.5 and 2.6 points, respectively. The code of UniK-QA is available at: \url{https://github.com/facebookresearch/UniK-QA}.",
}
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<abstract>We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retriever-reader model which has so far been limited to text sources only. Our approach greatly improves the results on knowledge-base QA tasks by 11 points, compared to latest graph-based methods. More importantly, we demonstrate that our unified knowledge (UniK-QA) model is a simple and yet effective way to combine heterogeneous sources of knowledge, advancing the state-of-the-art results on two popular question answering benchmarks, NaturalQuestions and WebQuestions, by 3.5 and 2.6 points, respectively. The code of UniK-QA is available at: https://github.com/facebookresearch/UniK-QA.</abstract>
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%0 Conference Proceedings
%T UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
%A Oguz, Barlas
%A Chen, Xilun
%A Karpukhin, Vladimir
%A Peshterliev, Stan
%A Okhonko, Dmytro
%A Schlichtkrull, Michael
%A Gupta, Sonal
%A Mehdad, Yashar
%A Yih, Scott
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F oguz-etal-2022-unik
%X We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retriever-reader model which has so far been limited to text sources only. Our approach greatly improves the results on knowledge-base QA tasks by 11 points, compared to latest graph-based methods. More importantly, we demonstrate that our unified knowledge (UniK-QA) model is a simple and yet effective way to combine heterogeneous sources of knowledge, advancing the state-of-the-art results on two popular question answering benchmarks, NaturalQuestions and WebQuestions, by 3.5 and 2.6 points, respectively. The code of UniK-QA is available at: https://github.com/facebookresearch/UniK-QA.
%R 10.18653/v1/2022.findings-naacl.115
%U https://aclanthology.org/2022.findings-naacl.115
%U https://doi.org/10.18653/v1/2022.findings-naacl.115
%P 1535-1546
Markdown (Informal)
[UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering](https://aclanthology.org/2022.findings-naacl.115) (Oguz et al., Findings 2022)
ACL
- Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Scott Yih. 2022. UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1535–1546, Seattle, United States. Association for Computational Linguistics.