  Most prior work on task-oriented dialogue systems are restricted to limited
coverage of domain APIs. However, users oftentimes have requests that are out
of the scope of these APIs. This work focuses on responding to these
beyond-API-coverage user turns by incorporating external, unstructured
knowledge sources. Our approach works in a pipelined manner with
knowledge-seeking turn detection, knowledge selection, and response generation
in sequence. We introduce novel data augmentation methods for the first two
steps and demonstrate that the use of information extracted from dialogue
context improves the knowledge selection and end-to-end performances. Through
experiments, we achieve state-of-the-art performance for both automatic and
human evaluation metrics on the DSTC9 Track 1 benchmark dataset, validating the
effectiveness of our contributions.
