论文标题
增强具有关系提取的以任务为导向的对话系统
Augmenting Task-Oriented Dialogue Systems with Relation Extraction
论文作者
论文摘要
标准面向任务的对话管道使用意图分类和插槽填充来解释用户话语。尽管这种方法可以处理各种查询，但它并未提取处理更复杂的查询所需的信息，以包含插槽之间的关系。我们建议将关系提取到这条管道中，这是扩展对话系统功能的有效方法。我们通过使用带有插槽和关系注释的内部数据集来评估我们的方法。最后，我们展示了一旦可用的表示相关注释的表达能力，如何简化插槽注释方案，从而减少了插槽的数量，同时仍捕获用户的预期含义。
The standard task-oriented dialogue pipeline uses intent classification and slot-filling to interpret user utterances. While this approach can handle a wide range of queries, it does not extract the information needed to handle more complex queries that contain relationships between slots. We propose integration of relation extraction into this pipeline as an effective way to expand the capabilities of dialogue systems. We evaluate our approach by using an internal dataset with slot and relation annotations spanning three domains. Finally, we show how slot-filling annotation schemes can be simplified once the expressive power of relation annotations is available, reducing the number of slots while still capturing the user's intended meaning.
