@inproceedings{wen-etal-2022-original,
title = "Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for {W}iki{H}ow{QA} Answer Selection Task",
author = "Wen, Liang and
Li, Juan and
Wang, Houfeng and
Luo, Yingwei and
Wang, Xiaolin and
Zhang, Xiaodong and
Cheng, Zhicong and
Yin, Dawei",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.133/",
pages = "1546--1555",
abstract = "Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a new dataset, WikiHowQA, which contains a corresponding reference summary for each original lengthy answer. And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances. However, when given a question and a set of long candidate answers, human beings could effortlessly identify the correct answer without the aid of additional answer summaries since the original answers contain all the information volume that answer summaries contain. In addition, pretrained language models have been shown superior or comparable to human beings on many natural language processing tasks. Motivated by those, we design a series of neural models, either pretraining-based or non-pretraining-based, to check wether the additional answer summaries are helpful for ranking the relevancy degrees of question-answer pairs on WikiHowQA dataset. Extensive automated experiments and hand analysis show that the additional answer summaries are not useful for achieving the best performance."
}
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<abstract>Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a new dataset, WikiHowQA, which contains a corresponding reference summary for each original lengthy answer. And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances. However, when given a question and a set of long candidate answers, human beings could effortlessly identify the correct answer without the aid of additional answer summaries since the original answers contain all the information volume that answer summaries contain. In addition, pretrained language models have been shown superior or comparable to human beings on many natural language processing tasks. Motivated by those, we design a series of neural models, either pretraining-based or non-pretraining-based, to check wether the additional answer summaries are helpful for ranking the relevancy degrees of question-answer pairs on WikiHowQA dataset. Extensive automated experiments and hand analysis show that the additional answer summaries are not useful for achieving the best performance.</abstract>
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%0 Conference Proceedings
%T Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task
%A Wen, Liang
%A Li, Juan
%A Wang, Houfeng
%A Luo, Yingwei
%A Wang, Xiaolin
%A Zhang, Xiaodong
%A Cheng, Zhicong
%A Yin, Dawei
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F wen-etal-2022-original
%X Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a new dataset, WikiHowQA, which contains a corresponding reference summary for each original lengthy answer. And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances. However, when given a question and a set of long candidate answers, human beings could effortlessly identify the correct answer without the aid of additional answer summaries since the original answers contain all the information volume that answer summaries contain. In addition, pretrained language models have been shown superior or comparable to human beings on many natural language processing tasks. Motivated by those, we design a series of neural models, either pretraining-based or non-pretraining-based, to check wether the additional answer summaries are helpful for ranking the relevancy degrees of question-answer pairs on WikiHowQA dataset. Extensive automated experiments and hand analysis show that the additional answer summaries are not useful for achieving the best performance.
%U https://aclanthology.org/2022.coling-1.133/
%P 1546-1555
Markdown (Informal)
[Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task](https://aclanthology.org/2022.coling-1.133/) (Wen et al., COLING 2022)
ACL
- Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, and Dawei Yin. 2022. Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1546–1555, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.