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

arXiv: https://arxiv.org/abs/2010.03450 · HTML: https://mlsanity.com/paper/2010.03450/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 30 of 36)

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- [Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://mlsanity.com/paper/2310.13290/) (2023)
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- [Efficiently Estimating Data Efficiency for Language Model Fine-tuning](https://mlsanity.com/paper/2512.24991/) (2025)
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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.