@inproceedings{ebert-etal-2026-reading,
    title = "Reading Between the Lines: The One-Sided Conversation Problem",
    author = "Ebert, Victoria  and
      Singh, Rishabh  and
      Chen, Tuochao  and
      Smith, Noah A.  and
      Gollakota, Shyamnath",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1757/",
    pages = "35227--35260",
    ISBN = "979-8-89176-395-1",
    abstract = "Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded. We formalize the one-sided conversation problem (1SC): inferring and learning from only one side of a conversation. We study two tasks: (1) reconstructing the missing speaker{'}s turns and (2) generating summaries from one-sided transcripts. Evaluating models on MultiWOZ, DailyDialog, SpokenWOZ and Candor with both human A/B testing and LLM-as-a-judge metrics, we find that additional context improves reconstruction, and while large models generate promising reconstructions with prompting, smaller models require finetuning. Further, high-quality summaries can be generated without reconstructing missing turns. We present 1SC as a novel challenge and report promising results that mark a step toward privacy-aware conversational AI."
}