@inproceedings{kim-etal-2023-semantic,
    title = "Semantic Ambiguity Detection in Sentence Classification using Task-Specific Embeddings",
    author = "Kim, Jong Myoung  and
      Lee, Young-jun  and
      Jung, Sangkeun  and
      Choi, Ho-jin",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.acl-industry.41/",
    doi = "10.18653/v1/2023.acl-industry.41",
    pages = "425--437",
    abstract = "Ambiguity 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 scenarios and training data. In addition, we apply task-specific 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"
}