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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>\n            Community Q&amp;A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&amp;A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users\u2019 efforts of raising new questions, they usually come with a\n            <jats:italic>question retrieval<\/jats:italic>\n            function, which retrieves similar existing questions (and their answers) to a user\u2019s query. However, it can be difficult for community Q&amp;A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely,\n            <jats:sans-serif>X-QR<\/jats:sans-serif>\n            , which consists of two modules\u2014a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&amp;A forums question retrieval. The experiment results suggest that the proposed\n            <jats:sans-serif>X-QR<\/jats:sans-serif>\n            model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&amp;A tasks.\n          <\/jats:p>","DOI":"10.1145\/3487291","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T11:27:44Z","timestamp":1641814064000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Adversarial Cross-domain Community Question Retrieval"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3896-7236","authenticated-orcid":false,"given":"Aibo","family":"Guo","sequence":"first","affiliation":[{"name":"National University of Defense Technology, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Pang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, Hunan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_2_2","DOI":"10.5555\/1609067.1609071"},{"doi-asserted-by":"publisher","key":"e_1_3_2_3_2","DOI":"10.5555\/1610075.1610094"},{"doi-asserted-by":"publisher","key":"e_1_3_2_4_2","DOI":"10.1142\/S0218001493000339"},{"key":"e_1_3_2_5_2","first-page":"273","volume-title":"Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP\u201911)","author":"Cai Li","year":"2011","unstructured":"Li Cai, Guangyou Zhou, Kang Liu, and Jun Zhao. 2011. 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