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arxiv: 2511.03056 · v2 · submitted 2025-11-04 · 💻 cs.CL · cs.AI· cs.LG

Reading Between the Lines: The One-Sided Conversation Problem

Victoria Ebert , Rishabh Singh , Tuochao Chen , Noah A. Smith , Shyamnath Gollakota This is my paper

Pith reviewed 2026-05-18 00:35 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords one-sided conversationsdialogue reconstructionconversation summarizationlarge language modelspromptingfinetuningprivacy-preserving AImissing turns
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The pith

Models reconstruct missing turns in one-sided conversations when given one future turn and utterance length information.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines the one-sided conversation problem as the task of inferring content from only one recorded speaker in settings where both sides cannot be captured. It examines two practical goals: filling in the unseen speaker's contributions for immediate use and creating summaries straight from the partial transcript. Experiments across standard dialogue collections show that supplying a single upcoming turn plus length details raises the accuracy of reconstructions. Inserting placeholder markers in prompts cuts down on invented content, large models handle the task through prompting, and smaller models gain from additional training. Summaries reach good quality even when the reconstruction step is omitted entirely.

Core claim

We formalize the one-sided conversation problem (1SC) and evaluate prompting and finetuned models on reconstructing the missing speaker's turns and on generating summaries from one-sided transcripts. On MultiWOZ, DailyDialog, and Candor, access to one future turn together with utterance length information improves reconstruction quality. Placeholder prompting reduces hallucination, large models produce promising results with prompting alone while smaller models require finetuning, and high-quality summaries can be generated without first reconstructing the missing turns.

What carries the argument

The one-sided conversation problem (1SC), which frames inference and learning from a single recorded side of a dialogue as the core task, carried by mechanisms of future-turn access, length metadata, and placeholder prompting.

If this is right

  • Reconstruction of missing turns becomes feasible for real-time applications when limited future context is available.
  • Placeholder tokens in prompts measurably lower the rate of fabricated content in generated turns.
  • Large models can perform reconstruction via prompting without task-specific training.
  • Conversation summaries can be produced at high quality while skipping the reconstruction stage.
  • The approach supports privacy-aware systems in domains where only one side of speech is recordable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same limited-context techniques could apply to other asymmetric audio capture settings such as wearable devices or single-mic meetings.
  • Adding uncertainty estimates to reconstructions would help decide when to trust the filled-in turns for downstream actions.
  • Direct summarization from one side may extend naturally to domains with incomplete speaker coverage beyond the tested datasets.

Load-bearing premise

The chosen datasets capture the structure and content of real-world one-sided conversations that arise in privacy-restricted environments.

What would settle it

Human raters would judge reconstructions produced with one future turn and length information as no better than those produced without that information, or one-sided summaries would receive lower quality ratings than summaries built from complete transcripts in direct comparison.

Figures

Figures reproduced from arXiv: 2511.03056 by Noah A. Smith, Rishabh Singh, Shyamnath Gollakota, Tuochao Chen, Victoria Ebert.

Figure 1
Figure 1. Figure 1: We introduce the one-sided conversation (1SC) problem: making inferences from only one side of a conversation transcript. We focus on reconstruction of the missing content and creating summaries of the whole one-sided conversation. A fundamental barrier, however, remains: in many real-world scenarios, only one side of a con￾versation is available for processing. This asymme￾try stems from both technical an… view at source ↗
Figure 2
Figure 2. Figure 2: Examples of different levels of context we con [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Using our extraction based metrics, we show [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example cases of our evaluation rubric for [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Human evaluation summary results. The masked-dialogue summary either outperformed the reconstructed-dialogue summary (DailyDialog) or per￾formed similarly (MultiWoz). content of Turn N, leading to a cascade of errors when given the full context (see §E). 4.3.2 Automated Evaluations Metrics. As before, we use both rubric scores and precision-recall metrics. Rubric scores. We adopt a blind review setup. The … view at source ↗
Figure 6
Figure 6. Figure 6: Results for summary evaluation. n = 1000 for DailyDialog, n = 1313 for MultiWOZ, n = 5 for Candor. Masked-dialogue summaries were consistently ranked above predicted-dialogue summaries, and had higher precision and comparable recall. as good if not better summaries than their recon￾structed counterparts, but the reconstructions lend a hand in the more task oriented conversations. We find that, as expected,… view at source ↗
Figure 7
Figure 7. Figure 7: The results of our rubric evaluation on DailyDialog and MultiWOZ. Full Masked Predicted [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper formalizes the one-sided conversation (1SC) problem for settings such as telemedicine and call centers where only one side of a dialogue is recorded. It evaluates two tasks—reconstructing missing speaker turns and generating summaries from one-sided transcripts—using prompting and finetuning on MultiWOZ, DailyDialog, and Candor datasets. Evaluations combine human A/B testing and LLM-as-a-judge metrics, with reported findings that access to one future turn plus utterance-length information improves reconstruction, placeholder prompting mitigates hallucination, large models succeed with prompting while smaller models require finetuning, and high-quality summaries can be produced without full reconstruction.

Significance. If the empirical trends hold under closer scrutiny of prompt details and statistical tests, the work provides a useful empirical baseline for privacy-aware dialogue systems. The use of three public datasets together with dual human/LLM evaluation is a strength that supports reproducibility and allows direct comparison of prompting versus finetuning regimes.

major comments (3)
  1. [Abstract and §1] Abstract and §1 (motivation): the central claim that the reconstruction results address 'real-time use cases' is undercut by the experimental provision of one future turn. In a genuine streaming one-sided setting only the recorded side up to the current time is observable; future turns are unavailable by definition. The reported gains therefore characterize an offline or delayed regime rather than the real-time regime invoked in the problem statement.
  2. [Experimental setup] Experimental setup (reconstruction task): the paper does not report an ablation that removes all future information while retaining only past and current recorded-side context. Without this control, it is unclear how much of the reported improvement is attributable to the one-sided constraint versus the leakage of future context.
  3. [§4] §4 (evaluation): while dual human/LLM metrics are used, the manuscript does not report inter-annotator agreement, exact prompt templates for the LLM judge, or statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) on the A/B preference differences. These omissions make it difficult to assess whether the observed trends are robust.
minor comments (2)
  1. The weakest-assumption note in the reader report—that MultiWOZ, DailyDialog, and Candor may not represent real-world one-sided distributions in telemedicine or call centers—is a scope limitation rather than a correctness error; a brief discussion of domain shift would strengthen the paper.
  2. Notation for 'utterance length' and 'placeholder prompting' should be defined once in a dedicated subsection rather than introduced inline.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important clarifications needed in our presentation of the one-sided conversation problem. We address each major comment below and will incorporate revisions to improve the manuscript's precision and rigor.

read point-by-point responses

  1. Referee: [Abstract and §1] Abstract and §1 (motivation): the central claim that the reconstruction results address 'real-time use cases' is undercut by the experimental provision of one future turn. In a genuine streaming one-sided setting only the recorded side up to the current time is observable; future turns are unavailable by definition. The reported gains therefore characterize an offline or delayed regime rather than the real-time regime invoked in the problem statement.

    Authors: We agree that providing access to one future turn means the reported reconstruction results apply to a delayed or offline regime rather than a strictly streaming real-time setting. In the revised manuscript we will update the abstract and Section 1 to explicitly distinguish these regimes, clarify that the one-future-turn condition approximates practical scenarios with minimal buffering (e.g., call-center recordings), and add a discussion of the additional challenges posed by purely real-time constraints with no future context. revision: yes

  2. Referee: [Experimental setup] Experimental setup (reconstruction task): the paper does not report an ablation that removes all future information while retaining only past and current recorded-side context. Without this control, it is unclear how much of the reported improvement is attributable to the one-sided constraint versus the leakage of future context.

    Authors: This is a valid concern. We will add a new ablation experiment that uses only past and current recorded-side context with no future turns at all. This control condition will allow us to isolate the contribution of the one-sided constraint itself from any benefit due to future-context leakage and will be reported alongside the existing results. revision: yes

  3. Referee: [§4] §4 (evaluation): while dual human/LLM metrics are used, the manuscript does not report inter-annotator agreement, exact prompt templates for the LLM judge, or statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) on the A/B preference differences. These omissions make it difficult to assess whether the observed trends are robust.

    Authors: We acknowledge these evaluation details were omitted. In the revision we will report inter-annotator agreement for the human A/B tests, include the exact LLM-judge prompt templates in an appendix, and add statistical significance testing (paired t-tests and/or bootstrap confidence intervals) on the preference differences to substantiate the robustness of the observed trends. revision: yes

Circularity Check

0 steps flagged

No circularity in empirical evaluation on public benchmarks

full rationale

The paper presents an empirical study of prompting and finetuning for one-sided conversation reconstruction and summarization on the public datasets MultiWOZ, DailyDialog, and Candor. No equations, derivations, or self-defined parameters appear; all reported improvements (e.g., gains from one future turn or placeholder prompting) are measured outcomes on held-out data rather than quantities fitted or renamed by construction. Claims rest on standard experimental protocols and external metrics (human A/B tests, LLM judges) without load-bearing self-citations or ansatzes that reduce the result to its inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard NLP assumptions about dialogue predictability and the reliability of LLM judges rather than new free parameters or invented entities.

axioms (2)
  • domain assumption Dialogue turns exhibit predictable structure and length patterns that can be inferred from one side plus limited context.
    Invoked when claiming that one future turn and utterance length improve reconstruction.
  • domain assumption LLM-as-a-judge metrics align sufficiently with human judgments for evaluating reconstruction and summary quality.
    Used to support the reported performance findings alongside human A/B testing.

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Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages · 2 internal anchors

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    You will not be penalized for choosing 0, but please use it sparingly. Some responses include XXXXXX rather than specific names, places, or numbers. Please treat these as normal words in the conversation, as if they were names, places or numbers. Thank you for your participation! C Additional Figures Fig. 7 shows the rubric scores for our summariza- tion ...

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