将基于文本的对话状态追踪器适配于口语对话
Adapting Text-based Dialogue State Tracker for Spoken Dialogues
AI总结：
本文提出一种适配口语对话的模型，包含自动语音识别纠错、基于D3ST的槽位估计及后处理模块，验证了纠错与数据增强对提升鲁棒性的关键作用。
摘要：
尽管通过对话系统技术竞赛（DSTC）在对话系统方面取得了显著进展，但构建一个具有语音接口的鲁棒任务型对话系统仍然是关键挑战之一。由于拥有丰富书面语料库的数据集，大多数进展都是在基于文本的对话系统上取得的，而口语对话数据集非常稀缺。然而，正如Siri和Alexa等语音助手系统所示，将这种成功转移到口语对话具有重要的实际意义。在本文中，我们描述了在DSTC11的语音感知对话系统技术挑战赛中构建并成功参赛的模型所做的工程努力。我们的模型由三个主要模块组成：（1）自动语音识别错误纠正，以弥合口语与文本话语之间的差距；（2）基于文本的对话系统（D3ST），利用槽位描述来估计槽位和值；（3）后处理，用于恢复估计槽位值的错误。实验表明，使用显式的自动语音识别错误纠正模块、后处理和数据增强，对于将基于文本的对话状态追踪器适配到口语对话语料库至关重要。
英文摘要：
Although there have been remarkable advances in dialogue systems through the dialogue systems technology competition (DSTC), it remains one of the key challenges to building a robust task-oriented dialogue system with a speech interface. Most of the progress has been made for text-based dialogue systems since there are abundant datasets with written corpora while those with spoken dialogues are very scarce. However, as can be seen from voice assistant systems such as Siri and Alexa, it is of practical importance to transfer the success to spoken dialogues. In this paper, we describe our engineering effort in building a highly successful model that participated in the speech-aware dialogue systems technology challenge track in DSTC11. Our model consists of three major modules: (1) automatic speech recognition error correction to bridge the gap between the spoken and the text utterances, (2) text-based dialogue system (D3ST) for estimating the slots and values using slot descriptions, and (3) post-processing for recovering the error of the estimated slot value. Our experiments show that it is important to use an explicit automatic speech recognition error correction module, post-processing, and data augmentation to adapt a text-based dialogue state tracker for spoken dialogue corpora.
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。
