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Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge

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arxiv 2104.06828 v1 pith:XZALSNNM submitted 2021-04-14 cs.CL

Bodhisattwa Prasad Majumder , Sudha Rao , Michel Galley , Julian McAuley This is my paper · ORCID
classification cs.CL
keywords whatmissingusefulclarificationcontextglobalidentifymodel
verification ladder T0 review T1 audit T2 compute T3 formal
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The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Referential ambiguity and clarification requests: comparing human and LLM behaviour

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Humans seldom ask clarification questions for referential ambiguity, while LLMs ask them more often, and reasoning prompts increase LLM question frequency and relevance.

  2. Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction

    cs.LG 2025-05 reject novelty 4.0 of 10

    A multi-task user satisfaction model with SimCSE-style contrastive learning and domain-intent classification shows small gains on DuerOS, but test-set threshold tuning and input-label leakage weaken the evidence.

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