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"I'd rather just go to bed": Understanding Indirect Answers

arXiv 2010.03450 ↗First posted 7 Oct 2020cs.CLcited by 36

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Abstract (arXiv)

We revisit a pragmatic inference problem in dialog: understanding indirect responses to questions. Humans can interpret 'I'm starving.' in response to 'Hungry?', even without direct cue words such as 'yes' and 'no'. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today's systems are only as sensitive to these pragmatic moves as their language model allows. We create and release the first large-scale English language corpus 'Circa' with 34,268 (polar question, indirect answer) pairs to enable progress on this task. The data was collected via elaborate crowdsourcing, and contains utterances with yes/no meaning, as well as uncertain, middle-ground, and conditional responses. We also present BERT-based neural models to predict such categories for a question-answer pair. We find that while transfer learning from entailment works reasonably, performance is not yet sufficient for robust dialog. Our models reach 82-88% accuracy for a 4-class distinction, and 74-85% for 6 classes.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.

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Citing papers per year: 2021: 6; 2022: 7; 2023: 5; 2024: 7; 2025: 8; 2026: 3.

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  1. 2021MetaICL: Learning to Learn In Contextcited by 450
  2. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  3. 2024PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilitiescited by 58
  4. 2022(QA)2: Question Answering with Questionable Assumptionscited by 51
  5. 2023Pre-Training to Learn in Contextcited by 41
  6. 2021Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learningcited by 35
  7. 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challengescited by 31
  8. 2021Investigating Robustness of Dialog Models to Popular Figurative Language Constructscited by 19
  9. 2024Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystackcited by 19
  10. 2024Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Trackingcited by 18
  11. 2022Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Expertscited by 12
  12. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
24 more
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  2. 2025Entailed Between the Lines: Incorporating Implication into NLIcited by 9
  3. 2025Always Tell Me The Odds: Fine-grained Conditional Probability Estimationcited by 9
  4. 2024Are LLMs good pragmatic speakers?cited by 7
  5. 2022Few-shot Adaptation Works with UnpredicTable Datacited by 6
  6. 2023Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learningcited by 5
  7. 2024VAGUE: Visual Contexts Clarify Ambiguous Expressionscited by 5
  8. 2021"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systemscited by 3
  9. 2022Resolving Indirect Referring Expressions for Entity Selectioncited by 3
  10. 2025You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Modelscited by 3
  11. 2025Understand the Implication: Learning to Think for Pragmatic Understandingcited by 3
  12. 2023Interpreting Indirect Answers to Yes-No Questions in Multiple Languagescited by 2
  13. 2022KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLPcited by 1
  14. 2022Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Delta Tuningcited by 1
  15. 2024Controllable Decontextualization of Yes/No Question and Answers into Factual Statementscited by 1
  16. 2024Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domainscited by 1
  17. 2025Efficiently Estimating Data Efficiency for Language Model Fine-tuningcited by 1
  18. 2026Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alikecited by 1
  19. 2021Towards Handling Unconstrained User Preferences in Dialoguecited by 0
  20. 2022N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Modelscited by 0
  21. 2023Interpreting Answers to Yes-No Questions in User-Generated Contentcited by 0
  22. 2025An Interdisciplinary Review of Commonsense Reasoning and Intent Detectioncited by 0
  23. 2026DRInQ: Evaluating Conversational Implicature with Controlled Context Variationcited by 0
  24. 2026Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellationcited by 0