@inproceedings{zhao-etal-2019-simple,
title = "Simple Question Answering with Subgraph Ranking and Joint-Scoring",
author = "Zhao, Wenbo and
Chung, Tagyoung and
Goyal, Anuj and
Metallinou, Angeliki",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1029",
doi = "10.18653/v1/N19-1029",
pages = "324--334",
abstract = "Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject{--}relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject{--}relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44{\%} in accuracy) on the SimpleQuestions dataset.",
}
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<abstract>Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject–relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject–relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.</abstract>
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%0 Conference Proceedings
%T Simple Question Answering with Subgraph Ranking and Joint-Scoring
%A Zhao, Wenbo
%A Chung, Tagyoung
%A Goyal, Anuj
%A Metallinou, Angeliki
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zhao-etal-2019-simple
%X Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject–relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject–relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.
%R 10.18653/v1/N19-1029
%U https://aclanthology.org/N19-1029
%U https://doi.org/10.18653/v1/N19-1029
%P 324-334
Markdown (Informal)
[Simple Question Answering with Subgraph Ranking and Joint-Scoring](https://aclanthology.org/N19-1029) (Zhao et al., NAACL 2019)
ACL
- Wenbo Zhao, Tagyoung Chung, Anuj Goyal, and Angeliki Metallinou. 2019. Simple Question Answering with Subgraph Ranking and Joint-Scoring. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 324–334, Minneapolis, Minnesota. Association for Computational Linguistics.