{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:34:03Z","timestamp":1782930843496,"version":"3.54.5"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MSIT (Ministry of Science, ICT), Korea"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Emotion recognition in conversation (ERC) is receiving more and more attention, as interactions between humans and machines increase in a variety of services such as chat-bot and virtual assistants. As emotional expressions within a conversation can heavily depend on the contextual information of the participating speakers, it is important to capture self-dependency and inter-speaker dynamics. In this study, we propose a new pre-trained model, SAPBERT, that learns to identify speakers in a conversation to capture the speaker-dependent contexts and address the ERC task. SAPBERT is pre-trained with three training objectives including Speaker Classification (SC), Masked Utterance Regression (MUR), and Last Utterance Generation (LUG). We investigate whether our pre-trained speaker-aware model can be leveraged for capturing speaker-dependent contexts for ERC tasks. Experiments show that our proposed approach outperforms baseline models through demonstrating the effectiveness and validity of our method.<\/jats:p>","DOI":"10.3390\/a16010008","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:26:25Z","timestamp":1671765985000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["SAPBERT: Speaker-Aware Pretrained BERT for Emotion Recognition in Conversation"],"prefix":"10.3390","volume":"16","author":[{"given":"Seunguook","family":"Lim","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Dongguk University Seoul, 30 Pildong-ro 1-gil, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2358-4021","authenticated-orcid":false,"given":"Jihie","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Dongguk University Seoul, 30 Pildong-ro 1-gil, Seoul 04620, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref_1","unstructured":"Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., and Morency, L.P. 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