论文标题
说不，是一门艺术：无法回答的对话查询的上下文化回答
Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries
论文作者
论文摘要
尽管在过去的十年中，端到端的神经系统在以任务为导向以及基于CHAT的对话系统中取得了重大进展，但大多数对话系统依赖于使用基于规则的，检索和生成性方法组合来生成一组排名响应的混合方法。这样的对话系统需要依靠后备机制来响应在对话框系统范围内无法回答的室外或新颖的用户查询。虽然，今天的对话系统依赖于静态和不自然的响应，例如“我不知道该问题的答案”或“我不确定”，但我们设计了一种神经方法，该神经方法会产生响应，这些响应在用户查询以及对用户拒绝的情况下对上下文意识到。这种自定义的响应提供了释义能力和上下文化，并改善了与用户的互动并减少对话单调性。我们的简单方法利用了对依赖性解释的规则，以及对问题响应对的合成数据进行微调的文本到文本变压器，从而产生了高度相关，语法和不同的问题。我们执行自动和手动评估以证明系统的功效。
Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rule-based, retrieval and generative approaches for generating a set of ranked responses. Such dialogue systems need to rely on a fallback mechanism to respond to out-of-domain or novel user queries which are not answerable within the scope of the dialog system. While, dialog systems today rely on static and unnatural responses like "I don't know the answer to that question" or "I'm not sure about that", we design a neural approach which generates responses which are contextually aware with the user query as well as say no to the user. Such customized responses provide paraphrasing ability and contextualization as well as improve the interaction with the user and reduce dialogue monotonicity. Our simple approach makes use of rules over dependency parses and a text-to-text transformer fine-tuned on synthetic data of question-response pairs generating highly relevant, grammatical as well as diverse questions. We perform automatic and manual evaluations to demonstrate the efficacy of the system.
