Keyword Extraction for Improved Document Retrieval in Conversational Search
This work addresses the problem of improving document retrieval in conversational search for users and systems, but it appears incremental as it builds on existing neural methods and datasets.
The paper tackles the challenge of incorporating user-provided information from multi-turn conversations into document retrieval by collecting two conversational keyword extraction datasets and proposing an end-to-end pipeline. The result shows that their approach beats state-of-the-art IR models, though specific performance numbers are not provided in the abstract.
Recent research has shown that mixed-initiative conversational search, based on the interaction between users and computers to clarify and improve a query, provides enormous advantages. Nonetheless, incorporating additional information provided by the user from the conversation poses some challenges. In fact, further interactions could confuse the system as a user might use words irrelevant to the information need but crucial for correct sentence construction in the context of multi-turn conversations. To this aim, in this paper, we have collected two conversational keyword extraction datasets and propose 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 and human annotation. Finally, we study the effect of keyword extraction on the end-to-end neural IR performance and show that our approach beats state-of-the-art IR models. We make the two datasets publicly available to foster research in this area.