Keyword Extraction for Improved Document Retrieval in Conversational Search
arXiv:2109.05979
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.
Accepted in IIR 2021
References in corpus (6)
- Sequence to Sequence Learning with Neural Networks
- A Deep Relevance Matching Model for Ad-hoc Retrieval
- Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search
- ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
- Ranking Clarifying Questions Based on Predicted User Engagement
- Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions