LARD：大规模人工不流利产生
LARD: Large-scale Artificial Disfluency Generation
摘要：
不流利性检测是实时对话系统中的一项关键任务。然而，尽管其重要性，它仍然是一个相对未开发的领域，主要是由于缺乏适当的数据集。与此同时，现有的数据集存在各种问题，包括类不平衡问题，这会显著影响模型在稀有类上的性能，正如本文所展示的那样。为此，我们提出LARD，一种方法，用于产生复杂和现实的人工不流畅，很少的努力。所提出的方法可以处理三种最常见的不流利：重复，替换和重新开始。此外，我们发布了一个新的大规模数据集，可以用于四个不同的任务：不流利检测，分类，提取和校正。LARD数据集上的实验结果表明，该方法产生的数据可以有效地用于检测和消除不流畅，同时也解决了现有数据集的局限性。
英文摘要：
Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.
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。
