Relevance feedback techniques assume that users provide relevance judgments for the top k (usually 10) documents and then re-rank using a new query model based on those judgments. Even though this is effective, there has been little research recently on this topic because requiring users to provide substantial feedback on a result list is impractical in a typical web search scenario. In new environments such as voice-based search with smart home devices, however, feedback about result quality can potentially be obtained during users' interactions with the system. Since there are severe limitations on the length and number of results that can be presented in a single interaction in this environment, the focus should move from browsing result lists to iterative retrieval and from retrieving documents to retrieving answers. In this paper, we study iterative relevance feedback techniques with a focus on retrieving answer passages. We first show that iterative feedback can be at least as effective as the top-k approach on standard TREC collections, and more effective on answer passage collections. We then propose an iterative feedback model for answer passages based on semantic similarity at passage level and show that it can produce significant improvements compared to both word-based iterative feedback models and those based on term-level semantic similarity.
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Revisiting Iterative Relevance Feedback for Document and Passage Retrieval
As more and more search traffic comes from mobile phones, intelligent assistants, and smart-home devices, new challenges (e.g., limited presentation space) and opportunities come up in information retrieval. Previously, an effective technique, relevance feedback (RF), has rarely been used in real search scenarios due to the overhead of collecting users’ relevance judgments. However, since users tend to interact more with the search results shown on the new interfaces, it becomes feasible to obtain users’ assessments on a few results during each interaction. This makes iterative relevance feedback (IRF) techniques look promising today. IRF can deal with a simplified scenario of conversational search, where the system asks users to provide relevance feedback on results shown in the current iteration and shows more relevant results in the next interaction. IRF has not been studied systematically in the new search scenarios and its effectiveness is mostly unknown. In this paper, we re-visit IRF and extend it with RF models proposed in recent years. We conduct extensive experiments to analyze and compare IRF with the standard top-k RF framework on document and passage retrieval. Experimental results show that IRF is at least as effective as the standard top-k RF framework for documents and much more effective for passages. This indicates that IRF for passage retrieval has huge potential and is a promising direction for conversational search based on relevance feedback.
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- Award ID(s):
- 1715095
- NSF-PAR ID:
- 10144169
- Date Published:
- Journal Name:
- SIGIR Workshop on Conversational Interaction Systems (WCIS'19)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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