  Successful conversational search systems can present natural, adaptive and
interactive shopping experience for online shopping customers. However,
building such systems from scratch faces real word challenges from both
imperfect product schema/knowledge and lack of training dialog data.In this
work we first propose ConvSearch, an end-to-end conversational search system
that deeply combines the dialog system with search. It leverages the text
profile to retrieve products, which is more robust against imperfect product
schema/knowledge compared with using product attributes alone. We then address
the lack of data challenges by proposing an utterance transfer approach that
generates dialogue utterances by using existing dialog from other domains, and
leveraging the search behavior data from e-commerce retailer. With utterance
transfer, we introduce a new conversational search dataset for online shopping.
Experiments show that our utterance transfer method can significantly improve
the availability of training dialogue data without crowd-sourcing, and the
conversational search system significantly outperformed the best tested
baseline.
