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.

翻译：标准的任务导向对话管道使用意图分类和填补时间档来解释用户的语句。虽然这种方法可以处理广泛的查询,但并不提取处理包含空档关系的更复杂查询所需的信息。我们提议将提取关系纳入该管道,作为扩大对话系统能力的有效途径。我们通过使用包含三个领域的空档和关系说明的内部数据集来评估我们的方法。最后,我们表明,一旦有关系说明的明示力量,填补空档说明计划如何可以简化,减少空档数量,同时仍然抓住用户的预期含义。