论文标题
人造反射检测，嗯，群众产生了差异
Artificial Disfluency Detection, Uh No, Disfluency Generation for the Masses
论文作者
论文摘要
现有的反射检测方法通常需要存在大的注释数据集。但是，此任务的当前数据集受到限制，遭受了阶级失衡的困扰，并且缺乏在现实世界情景中可能遇到的一些类型的障碍。这项工作提出了猪油，这是一种自动从流利文本中产生人工裂开的方法。猪油可以基于reparandum/repregnum注释方案模拟所有不同类型的分裂（重复，更换和重新启动）。此外，它还将上下文嵌入到了际交往的生成中，以产生现实的上下文感知人造崩溃。由于所提出的方法仅需要流利的文本，因此可以直接用于训练，绕过带注释的分歧数据的要求。我们的经验评估表明，当没有或只有少数数据可用时，确实可以有效地使用猪油。此外，我们的详细分析表明，所提出的方法会产生逼真的崩溃，并提高现有探测器的准确性。
Existing approaches for disfluency detection typically require the existence of large annotated datasets. However, current datasets for this task are limited, suffer from class imbalance, and lack some types of disfluencies that can be encountered in real-world scenarios. This work proposes LARD, a method for automatically generating artificial disfluencies from fluent text. LARD can simulate all the different types of disfluencies (repetitions, replacements and restarts) based on the reparandum/interregnum annotation scheme. In addition, it incorporates contextual embeddings into the disfluency generation to produce realistic context-aware artificial disfluencies. Since the proposed method requires only fluent text, it can be used directly for training, bypassing the requirement of annotated disfluent data. Our empirical evaluation demonstrates that LARD can indeed be effectively used when no or only a few data are available. Furthermore, our detailed analysis suggests that the proposed method generates realistic disfluencies and increases the accuracy of existing disfluency detectors.
