OLISIA：用于口语对话状态跟踪的级联系统
OLISIA: a Cascade System for Spoken Dialogue State Tracking
AI总结：
本文提出OLISIA级联系统，集成ASR与DST模型，通过归一化ASR输出、数据增强及增大模型规模，在DSTC11 Track 3中排名第一，有效缩小了书面与口语对话的性能差距。
摘要：
尽管对话状态跟踪（DST）是口语对话系统的核心组成部分，但近期关于该任务的研究大多处理聊天语料，忽视了口语与书面语言之间的差异。在本文中，我们提出了OLISIA，一个集成了自动语音识别（ASR）模型和对话状态跟踪（DST）模型的级联系统。我们在ASR和DST模块中引入了若干适应性调整，以提高对口语对话的集成性和鲁棒性。通过这些调整，我们的系统在评估口语DST的基准测试DSTC11 Track 3中排名第一。我们对结果进行了深入分析，发现对ASR输出进行归一化、通过数据增强调整DST输入，以及增大预训练模型的规模，均在减少书面与口语对话之间性能差异方面发挥了重要作用。
英文摘要：
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language.In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model. We introduce several adaptations in the ASR and DST modules to improve integration and robustness to spoken conversations.With these adaptations, our system ranked first in DSTC11 Track 3, a benchmark to evaluate spoken DST. We conduct an in-depth analysis of the results and find that normalizing the ASR outputs and adapting the DST inputs through data augmentation, along with increasing the pre-trained models size all play an important role in reducing the performance discrepancy between written and spoken conversations.
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。
