“我宁愿去睡觉”：理解间接答案
"I'd rather just go to bed": Understanding Indirect Answers
摘要：
我们重新审视对话中的语用推理问题：理解对问题的间接回答。人类可以解释“我饿了。”在回答“饿了吗？”，即使没有直接的提示词，如“是”和“否”。在对话系统中，允许自然的反应而不是封闭的词汇表将同样有益。然而，今天的系统对这些实用性的动作的敏感程度仅限于它们的语言模型所允许的范围。我们创建并发布了第一个大型英语语料库“Circa”，其中包含34，268对（极性问题，间接答案），以实现这项任务的进展。这些数据是通过精心设计的众包收集的，包含有是/否含义的话语，以及不确定的、中间立场的和有条件的反应。我们还提出了基于BERT的神经模型来预测问答对的类别。我们发现，虽然从蕴涵的迁移学习工作合理，性能还不足以强大的对话。我们的模型达到82-88%的准确度为4类的区别，和74-85%为6类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
