Semantic Ambiguity Detection in Sentence Classification using Task-Specific Embeddings

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dc.contributor.authorKim, Jongmyoungko
dc.contributor.authorLee, Young-Junko
dc.contributor.authorJung, Sang-Keunko
dc.contributor.authorChoi, Ho-Jinko
dc.date.accessioned2023-08-10T02:00:32Z-
dc.date.available2023-08-10T02:00:32Z-
dc.date.created2023-06-22-
dc.date.issued2023-07-10-
dc.identifier.citationThe 61st Annual Meeting of the Association for Computational Linguistics (ACL’23)-
dc.identifier.urihttp://hdl.handle.net/10203/311372-
dc.description.abstractAmbiguity is a major obstacle to providing services based on sentence classification. However, because of the structural limitations of the service, there may not be sufficient contextual information to resolve the ambiguity. In this situation, we focus on ambiguity detection so that service design considering ambiguity is possible. We utilize similarity in a semantic space to detect ambiguity in service scenarios1 and training data. In addition, we apply taskspecific embedding to improve performance. Our results demonstrate that ambiguities and resulting labeling errors in training data or scenarios can be detected. Additionally, we confirm that it can be used to debug services.-
dc.languageEnglish-
dc.publisherThe 61st Annual Meeting of the Association for Computational Linguistics-
dc.titleSemantic Ambiguity Detection in Sentence Classification using Task-Specific Embeddings-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationnameThe 61st Annual Meeting of the Association for Computational Linguistics (ACL’23)-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationToronto-
dc.contributor.localauthorChoi, Ho-Jin-
dc.contributor.nonIdAuthorJung, Sang-Keun-
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CS-Conference Papers(학술회의논문)
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