论文标题
面向任务的对话框无监督的插槽模式诱导
Unsupervised Slot Schema Induction for Task-oriented Dialog
论文作者
论文摘要
精心设计的模式描述了如何收集和注释对话框COLPORA是建立面向任务的对话系统的先决条件。在实际应用中，手动设计模式可能是错误的，艰苦的，迭代的，并且速度缓慢，尤其是当模式复杂时。为了减轻这一昂贵且耗时的过程，我们提出了一种无标记的对话Corpora诱导插槽模式的方法。我们的数据驱动方法利用内域语言模型和无监督的解析结构提取了候选插槽而无需限制，然后进行粗到细聚类以诱导插槽类型。我们将我们的方法与几个强有力的监督基线进行了比较，并显示了多沃兹和SGD数据集的SLOT模式诱导性能的显着提高。我们还证明了诱导模式在下游应用程序中的有效性，包括对话状态跟踪和响应产生。
Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated. To alleviate this expensive and time consuming process, we propose an unsupervised approach for slot schema induction from unlabeled dialog corpora. Leveraging in-domain language models and unsupervised parsing structures, our data-driven approach extracts candidate slots without constraints, followed by coarse-to-fine clustering to induce slot types. We compare our method against several strong supervised baselines, and show significant performance improvement in slot schema induction on MultiWoz and SGD datasets. We also demonstrate the effectiveness of induced schemas on downstream applications including dialog state tracking and response generation.
