@inproceedings{qin-etal-2023-webcpm,
title = "{W}eb{CPM}: Interactive Web Search for {C}hinese Long-form Question Answering",
author = "Qin, Yujia and
Cai, Zihan and
Jin, Dian and
Yan, Lan and
Liang, Shihao and
Zhu, Kunlun and
Lin, Yankai and
Han, Xu and
Ding, Ning and
Wang, Huadong and
Xie, Ruobing and
Qi, Fanchao and
Liu, Zhiyuan and
Sun, Maosong and
Zhou, Jie",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.499",
doi = "10.18653/v1/2023.acl-long.499",
pages = "8968--8988",
abstract = "Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 15,372 supporting facts and 125,954 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5{\%} and 47.5{\%} of the cases on our dataset and DuReader, respectively. The interface, dataset, and codes are publicly available at \url{https://github.com/thunlp/WebCPM}.",
}
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<abstract>Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 15,372 supporting facts and 125,954 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively. The interface, dataset, and codes are publicly available at https://github.com/thunlp/WebCPM.</abstract>
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%0 Conference Proceedings
%T WebCPM: Interactive Web Search for Chinese Long-form Question Answering
%A Qin, Yujia
%A Cai, Zihan
%A Jin, Dian
%A Yan, Lan
%A Liang, Shihao
%A Zhu, Kunlun
%A Lin, Yankai
%A Han, Xu
%A Ding, Ning
%A Wang, Huadong
%A Xie, Ruobing
%A Qi, Fanchao
%A Liu, Zhiyuan
%A Sun, Maosong
%A Zhou, Jie
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F qin-etal-2023-webcpm
%X Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two procedures: information retrieval, which searches for relevant supporting facts, and information synthesis, which integrates these facts into a coherent answer. In this paper, we introduce WebCPM, the first Chinese LFQA dataset. One unique feature of WebCPM is that its information retrieval is based on interactive web search, which engages with a search engine in real time. Following WebGPT, we develop a web search interface. We recruit annotators to search for relevant information using our interface and then answer questions. Meanwhile, the web search behaviors of our annotators would be recorded. In total, we collect 5,500 high-quality question-answer pairs, together with 15,372 supporting facts and 125,954 web search actions. We fine-tune pre-trained language models to imitate human behaviors for web search and to generate answers based on the collected facts. Our LFQA pipeline, built on these fine-tuned models, generates answers that are no worse than human-written ones in 32.5% and 47.5% of the cases on our dataset and DuReader, respectively. The interface, dataset, and codes are publicly available at https://github.com/thunlp/WebCPM.
%R 10.18653/v1/2023.acl-long.499
%U https://aclanthology.org/2023.acl-long.499
%U https://doi.org/10.18653/v1/2023.acl-long.499
%P 8968-8988
Markdown (Informal)
[WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://aclanthology.org/2023.acl-long.499) (Qin et al., ACL 2023)
ACL
- Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, and Jie Zhou. 2023. WebCPM: Interactive Web Search for Chinese Long-form Question Answering. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8968–8988, Toronto, Canada. Association for Computational Linguistics.