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    <abstract>Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradigms are powerful for natural language understanding, which has the potential for better question modeling and expert finding. Inspired by this, we propose a CQA-domain Contrastive Pre-training framework for Expert Finding, named CPEF, which could learn more comprehensive question representations. Specifically, considering that there is semantic complementation between question titles and bodies, during the domain pre-training phase, we propose a title-body contrastive learning task to enhance question representations, which directly treats the question title and the corresponding body as positive samples of each other, instead of designing extra data-augmentation strategies. Furthermore, a personalized tuning network is proposed to inject the personalized preferences of different experts during the fine-tuning phase. Extensive experimental results on six real-world datasets demonstrate that our method could achieve superior performance for expert finding.</abstract>
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%0 Conference Proceedings
%T Contrastive Pre-training for Personalized Expert Finding
%A Peng, Qiyao
%A Liu, Hongtao
%A Lv, Zhepeng
%A Yang, Qing
%A Wang, Wenjun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F peng-etal-2023-contrastive
%X Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradigms are powerful for natural language understanding, which has the potential for better question modeling and expert finding. Inspired by this, we propose a CQA-domain Contrastive Pre-training framework for Expert Finding, named CPEF, which could learn more comprehensive question representations. Specifically, considering that there is semantic complementation between question titles and bodies, during the domain pre-training phase, we propose a title-body contrastive learning task to enhance question representations, which directly treats the question title and the corresponding body as positive samples of each other, instead of designing extra data-augmentation strategies. Furthermore, a personalized tuning network is proposed to inject the personalized preferences of different experts during the fine-tuning phase. Extensive experimental results on six real-world datasets demonstrate that our method could achieve superior performance for expert finding.
%R 10.18653/v1/2023.findings-emnlp.1058
%U https://aclanthology.org/2023.findings-emnlp.1058
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1058
%P 15797-15806

Markdown (Informal)

[Contrastive Pre-training for Personalized Expert Finding](https://aclanthology.org/2023.findings-emnlp.1058) (Peng et al., Findings 2023)

• Contrastive Pre-training for Personalized Expert Finding (Peng et al., Findings 2023)

ACL

• Qiyao Peng, Hongtao Liu, Zhepeng Lv, Qing Yang, and Wenjun Wang. 2023. Contrastive Pre-training for Personalized Expert Finding. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15797–15806, Singapore. Association for Computational Linguistics.