{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T15:59:15Z","timestamp":1757779155527,"version":"3.41.0"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,3,9]],"date-time":"2024-03-09T00:00:00Z","timestamp":1709942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0109703"],"award-info":[{"award-number":["2020AAA0109703"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076167"],"award-info":[{"award-number":["62076167"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Municipal Education Commission-Beijing Natural Fund Joint Funding Project","award":["KZ201910028039"],"award-info":[{"award-number":["KZ201910028039"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n            Social media produces large amounts of content every day. How to predict the potential influences of the contents from a social reply feedback perspective is a key issue that has not been explored. Thus, we propose a novel task named reply keyword prediction in social media, which aims to predict the keywords in the potential replies in as many aspects as possible. One prerequisite challenge is that the accessible social media datasets labeling such keywords remain absent. To solve this issue, we propose a new dataset,\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n            to study the reply keyword prediction in social media. This task could be seen as a single-turn dialogue keyword prediction for open-domain dialogue system. However, existing methods for dialogue keyword prediction cannot be adopted directly, which has two main drawbacks. First, they do not provide an explicit mechanism to model topic complementarity between keywords which is crucial in social media to controllably model all aspects of replies. Second, the collocations of keywords are not explicitly modeled, which also makes it less controllable to optimize for fine-grained prediction since the context information is much less than that in dialogue. To address these issues, we propose a two-stage disentangled framework, which can optimize the complementarity and collocation explicitly in a disentangled fashion. In the first stage, we use a sequence-to-set paradigm via multi-label prediction and determinantal point processes, to generate a set of keyword seeds satisfying the complementarity. In the second stage, we adopt a set-to-sequence paradigm via seq2seq model with the keyword seeds guidance from the set, to generate the more-fine-grained keywords with collocation. Experiments show that this method can generate not only a more diverse set of keywords but also more relevant and consistent keywords. Furthermore, the keywords obtained based on this method can achieve better reply generation results in the retrieval-based system than others.\n          <\/jats:p>","DOI":"10.1145\/3644074","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T07:02:50Z","timestamp":1707202970000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Seq2Set2Seq: A Two-stage Disentangled Method for Reply Keyword Generation in Social Media"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5953-4566","authenticated-orcid":false,"given":"Jie","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science, North China University of Technology, Beijing, China and China Language Intelligence Research Center, Capital Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4851-3927","authenticated-orcid":false,"given":"Yaguang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9053-9517","authenticated-orcid":false,"given":"Shizhu","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0978-0575","authenticated-orcid":false,"given":"Shun","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6083-8433","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7640-7022","authenticated-orcid":false,"given":"Shenping","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Unisound Information Technology Co., Ltd., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5003-0445","authenticated-orcid":false,"given":"Jiong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5368-339X","authenticated-orcid":false,"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science, North China University of Technology, Beijing, China and CNONIX National Standard Application and Promotion Lab, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,3,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1240"},{"key":"e_1_3_2_3_2","first-page":"7961","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Yuan Xingdi","year":"2018","unstructured":"Xingdi Yuan, Tong Wang, Rui Meng, Khushboo Thaker, Peter Brusilovsky, Daqing He, and Adam Trischler. 2018. 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