Zero-shot Query Reformulation for Conversational Search
- URL: http://arxiv.org/abs/2307.09384v2
- Date: Mon, 23 Oct 2023 17:24:02 GMT
- Title: Zero-shot Query Reformulation for Conversational Search
- Authors: Dayu Yang, Yue Zhang, Hui Fang
- Abstract summary: We introduce a novel Zero-shot Query Reformulation framework that reformulates queries based on previous dialogue contexts without requiring supervision from conversational search data.
Specifically, our framework utilizes language models designed for machine reading comprehension tasks to explicitly resolve two common ambiguities: coreference and omission, in raw queries.
It also provides greater explainability and effectively enhances query intent understanding because ambiguities are explicitly and proactively resolved.
- Score: 13.086953538245854
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As the popularity of voice assistants continues to surge, conversational
search has gained increased attention in Information Retrieval. However, data
sparsity issues in conversational search significantly hinder the progress of
supervised conversational search methods. Consequently, researchers are
focusing more on zero-shot conversational search approaches. Nevertheless,
existing zero-shot methods face three primary limitations: they are not
universally applicable to all retrievers, their effectiveness lacks sufficient
explainability, and they struggle to resolve common conversational ambiguities
caused by omission. To address these limitations, we introduce a novel
Zero-shot Query Reformulation (ZeQR) framework that reformulates queries based
on previous dialogue contexts without requiring supervision from conversational
search data. Specifically, our framework utilizes language models designed for
machine reading comprehension tasks to explicitly resolve two common
ambiguities: coreference and omission, in raw queries. In comparison to
existing zero-shot methods, our approach is universally applicable to any
retriever without additional adaptation or indexing. It also provides greater
explainability and effectively enhances query intent understanding because
ambiguities are explicitly and proactively resolved. Through extensive
experiments on four TREC conversational datasets, we demonstrate the
effectiveness of our method, which consistently outperforms state-of-the-art
baselines.
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