Papers with conversational dense retrieval

3 papers
Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding (2024.acl-long)

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Challenge: Conversational dense retrieval models lack interpretability, hindering intuitive understanding of model behaviors . a major limitation of conversational dense search is their lack of interpretability .
Approach: They propose to transform opaque session embeddings into explicit interpretable text . they propose to incorporate external interpretable query rewrites into the transformation process .
Outcome: The proposed approach yields more interpretable text and preserves original retrieval performance over baselines.
ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval (2022.emnlp-main)

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Challenge: Recent studies show that conversational dense retrieval is a promising technique for realizing conversational search, but its implementation is severely hindered by the lack of data.
Approach: They propose a method that transforms easily-accessible web search sessions into conversational search sessions to alleviate the data scarcity problem.
Outcome: The proposed method can transform easily-accessible web search sessions into conversational search sessions.
History-Aware Conversational Dense Retrieval (2024.findings-acl)

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Challenge: Current approaches for conversational dense retrieval rely on fine-tuning a pre-trained ad-hoc retriever, which can be lengthy and noisy.
Approach: They propose a context-denoised query reformulation and automatic mining of supervision signals based on historical turns.
Outcome: The proposed system improves on two public conversational search datasets.

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