Selective Weak Supervision for Neural Information Retrieval

28 Jan 2020Kaitao ZhangChenyan XiongZhenghao LiuZhiyuan Liu

This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-document relations approximate query-document relevance and propose a reinforcement weak supervision selection method, ReInfoSelect, which learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward... (read more)

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