RI: Small: Using Automatically Generated Paraphrases and Discriminative ASR Training to Author Robust Question-Answering Dialogue Systems

问答（QA）对话系统在虚拟角色的主要角色是回答人类用户提出的问题的广泛背景下是有用的。在基于QA的对话系统中，主要的解释任务可以被构造为将用户的问题与内容作者预期的一组问题进行匹配。 该项目首次研究了自动解释预期问题的方法如何帮助作者建立大量预期问题变体，从而可以显着提高聊天和口语的解释鲁棒性。 通过评估现有的虚拟病人对话系统和新的虚拟指南的哥伦布，俄亥俄州的COSI（科学和工业中心）科学博物馆的项目，该项目将加强医学教育，并提供一个鼓舞人心的例子，科学在行动中的儿童谁参加博物馆。 它还承诺提高在教育软件和商业QA系统的常见问题的简答评分的有效性。所提出的方法是第一个探索先进的自动释义技术的潜力，以提高解释的鲁棒性，在一个易于作者的QA对话系统。 通过在内容创作时采用释义，可以利用并明确作者对功能等同问题空间的知识，从而可能导致解释准确性的显着提高;此外，这样做可以为自动语音识别（ASR）训练建立一个有效的，与任务相关的歧视空间。 为了生成释义，该项目使用基于语法的表面实现器OpenCCG进行词汇-句法交替，以及广泛的覆盖资源，词义的向量空间模型和多词对齐。为了训练区别性的ASR模型，使问题的解释不同，生成的释义被纳入语义错误率估计。 使用从医学生和博物馆参观者收集的数据，该项目通过其对解释准确性和可用性的定性措施的影响来评估该方法。

Question-answering (QA) dialogue systems are useful in a broad range of contexts where the primary role of a virtual character is to answer questions posed by the human user. In QA-based dialogue systems, the primary interpretation task can be framed as matching a user's question against a set of questions anticipated by the content author. This project investigates for the first time how methods for automatically paraphrasing anticipated questions can aid authors in establishing a large set of expected question variants, making it possible to dramatically enhance interpretation robustness for both chatted and spoken language. By evaluating the project with an existing virtual patient dialogue system and new virtual guide for Columbus, Ohio's COSI (Center of Science and Industry) science museum, the project will enhance medical education and provide an inspirational example of science in action to the children who attend the museum. It also promises to enhance the effectiveness of short answer scoring in educational software and commercial QA systems for frequently asked questions.The proposed approach is the first to explore the potential of advanced automatic paraphrasing techniques to enhance the robustness of interpretation in an easy-to-author QA dialogue system. By employing paraphrasing at content authoring time, it becomes possible to take advantage of and make explicit the author's knowledge of the space of functionally equivalent questions, potentially leading to dramatic improvements in interpretation accuracy; furthermore, doing so makes it possible to set up an effective, task-relevant discrimination space for Automatic Speech Recognition (ASR) training. To generate paraphrases, the project uses the grammar-based surface realizer OpenCCG for lexico-syntactic alternations together with broad coverage resources, vector space models of word meaning and multiword alignments. To train discriminative ASR models that make a difference in question interpretation, generated paraphrases are incorporated into the semantic error rate estimation. Using data collected from medical students and museum visitors, the project assesses the approach via its impact on interpretation accuracy and qualitative measures of usability.