论文标题
“我宁愿上床睡觉”：理解间接答案
"I'd rather just go to bed": Understanding Indirect Answers
论文作者
论文摘要
我们在对话中重新审视了务实的推论问题：了解对问题的间接回答。人类可以解释“我正在挨饿”。为了回应“饥饿？”，即使没有直接提示词，例如“是”和“否”。在对话系统中，允许自然响应而不是封闭的词汇同样有益。但是，当今的系统对这些务实的动作的敏感程度与他们的语言模型所允许的敏感。我们创建并发布了第一个大规模的英语语料库“大约”，其中34,268（极性问题，间接答案）对这项任务进行了进展。数据是通过精心设计的众包收集的，其中包含具有“是/否含义的话语”，以及不确定的，中间的和有条件的响应。我们还提出了基于BERT的神经模型，以预测问题 - 答案对的类别。我们发现，在合理地转移学习的过程中，绩效还不足以实现强大的对话框。对于4级区别，我们的模型达到了82-88％的精度，而6个类别为74-85％。
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.
