一块砖足以打破口语对话状态追踪之墙吗？
Is one brick enough to break the wall of spoken dialogue state tracking?
AI总结：
本文提出一种完全神经化的口语对话状态追踪方法，并与级联方法深入比较，表明联合优化在音频原生场景下具有竞争力，同时指出上下文传播需考虑先前上下文的不确定性。
摘要：
在任务型对话（TOD）系统中，正确更新系统对用户请求的理解（即对话状态追踪）是实现顺畅交互的关键。传统上，TOD系统通过三个步骤完成这一更新：对用户话语进行转录、对关键概念进行语义提取，以及与先前已识别概念进行语境化。此类级联方法存在级联错误和分离优化的问题。端到端方法已被证明在轮次级语义提取步骤之前是有帮助的。本文更进一步，提供了：（1）一种完全神经化的口语对话状态追踪新方法；（2）与最先进的级联方法进行的深入比较；以及（3）改进上下文传播的途径。我们的研究强调，联合优化方法在上下文相关任务（如对话状态追踪，DST）中也具有竞争力，尤其是在音频原生设置下。DST系统中的上下文传播可以受益于考虑先前上下文固有不确定性的训练过程。
英文摘要：
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's requests (\textit{a.k.a} dialogue state tracking) is key to a smooth interaction. Traditionally, TOD systems perform this update in three steps: transcription of the user's utterance, semantic extraction of the key concepts, and contextualization with the previously identified concepts. Such cascade approaches suffer from cascading errors and separate optimization. End-to-End approaches have been proven helpful up to the turn-level semantic extraction step. This paper goes one step further and provides (1) a novel approach for completely neural spoken DST, (2) an in depth comparison with a state of the art cascade approach and (3) avenues towards better context propagation. Our study highlights that jointly-optimized approaches are also competitive for contextually dependent tasks, such as Dialogue State Tracking (DST), especially in audio native settings. Context propagation in DST systems could benefit from training procedures accounting for the previous' context inherent uncertainty.
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。
