@inproceedings{furuta-etal-2026-suppressing,
    title = "Suppressing Unnecessary Clarification Requests for Unknown Word Acquisition in Spoken Dialogue Using Syllable-Based {ASR} Confidence",
    author = "Furuta, Takumi  and
      Takeda, Ryu  and
      Komatani, Kazunori",
    editor = "Choi, Jinho D.  and
      Chen, Yun-Nung  and
      Funakoshi, Kotaro  and
      Emami, Ali",
    booktitle = "Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2026",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-workflow/2026.sigdial-1.4/",
    pages = "51--61",
    abstract = "Clarification requests are a promising way for spoken dialogue systems to acquire unknown words from users, but asking too often can burden users. Because unknown words are not in the system{'}s vocabulary, utterances must first be represented as syllable sequences, which are then segmented into words. In this setting, syllable-based automatic speech recognition (S-ASR) errors can cause utterances containing only known words to appear to contain unknown words, leading to unnecessary clarification requests. To address this issue within a stream-based active learning framework, we extend the reinforcement learning policy state with recognition reliability features. Specifically, we incorporate two confidence measures derived from S-ASR to make clarification request selection sensitive to S-ASR errors. We further incorporate segmentation confidence over N-best hypotheses to reduce the impact of minor S-ASR errors. Experiments using pre-recorded speech data showed that the number of clarification requests on utterances affected by S-ASR errors was reduced by 1.34. The area under the learning curve for word segmentation also numerically increased by 0.07."
}