  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 is more effective than
the top-k approach for answer retrieval. Then we propose an iterative feedback
model based on passage-level semantic match and show that it can produce
significant improvements compared to both word-based iterative feedback models
and those based on term-level semantic similarity.
