Reading Between the Lines: The One-Sided Conversation Problem

arXiv cs.CL Papers
conversational-ai dialogue-reconstruction privacy-aware one-sided-conversation multimodal-learning summarization

Summary

This paper introduces the one-sided conversation problem (1SC), addressing how to reconstruct missing dialogue and generate summaries when only one speaker's turns are available in real-world settings like telemedicine and call centers. The authors evaluate prompting and finetuned models on multiple datasets, finding that access to future context and utterance length information improves reconstruction, while high-quality summaries can be generated without full dialogue reconstruction.

arXiv:2511.03056v2 Announce Type: replace Abstract: Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call centers, and smart glasses. We formalize this as the one-sided conversation problem (1SC): inferring and learning from one side of a conversation. We study two tasks: (1) reconstructing the missing speaker's turns for real-time use cases, and (2) generating summaries from one-sided transcripts. Evaluating prompting and finetuned models on MultiWOZ, DailyDialog, and Candor with both human A/B testing and LLM-as-a-judge metrics, we find that access to one future turn and information about utterance length improves reconstruction, placeholder prompting helps to mitigate hallucination, 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.
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# Reading Between the Lines: The One-Sided Conversation Problem
Source: https://arxiv.org/abs/2511.03056
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> Abstract: Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call centers, and smart glasses. We formalize this as the one-sided conversation problem (1SC): inferring and learning from one side of a conversation. We study two tasks: (1) reconstructing the missing speaker's turns for real-time use cases, and (2) generating summaries from one-sided transcripts. Evaluating prompting and finetuned models on MultiWOZ, DailyDialog, and Candor with both human A/B testing and LLM-as-a-judge metrics, we find that access to one future turn and information about utterance length improves reconstruction, placeholder prompting helps to mitigate hallucination, 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.

## Submission history

From: Victoria Ebert [view email (https://arxiv.org/show-email/c68990da/2511.03056)] **[[v1]](https://arxiv.org/abs/2511.03056v1)** Tue, 4 Nov 2025 22:53:57 UTC (513 KB) **[v2]** Thu, 16 Apr 2026 17:48:38 UTC (9,889 KB)

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