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Recent research has shown that mixed-initiative conversational search, basedon the interaction between users and computers to clarify and improve a query,provides enormous advantages. Nonetheless, incorporating additional informationprovided by the user from the conversation poses some challenges. In fact,further interactions could confuse the system as a user might use wordsirrelevant to the information need but crucial for correct sentenceconstruction in the context of multi-turn conversations. To this aim, in thispaper, we have collected two conversational keyword extraction datasets andpropose an end-to-end document retrieval pipeline incorporating them.Furthermore, we study the performance of two neural keyword extraction models,namely, BERT and sequence to sequence, in terms of extraction accuracy andhuman annotation. Finally, we study the effect of keyword extraction on theend-to-end neural IR performance and show that our approach beatsstate-of-the-art IR models. We make the two datasets publicly available tofoster research in this area.

Keyword Extraction for Improved Document Retrieval in Conversational Search