论文标题
密集通道检索的隐式反馈：一种反事实方法
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
论文作者
论文摘要
在本文中，我们研究了如何有效利用密集猎犬（DRS）中的隐式反馈。我们认为从历史点击日志中单击数据可作为隐式反馈可用的特定情况。然后，我们利用这种历史性的隐性相互作用来提高DR的有效性。我们研究的一个关键挑战是点击信号中偏见（例如位置偏见）对DRS的影响。为了克服与存在这种偏见相关的问题，我们提出了反事实的Rocchio（Corocchio）算法来利用密集回收者中隐式反馈。我们在理论上和经验上都证明了用Corocchio学习的密集查询表示对位置偏见没有偏见，并带来了更高的检索效率。我们提供了所提出的方法和实验框架的实现，以及所有结果，网址为https://github.com/ielab/counterfactual-dr。
In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is available as implicit feedback. We then exploit such historic implicit interactions to improve the effectiveness of a DR. A key challenge that we study is the effect that biases in the click signal, such as position bias, have on the DRs. To overcome the problems associated with the presence of such bias, we propose the Counterfactual Rocchio (CoRocchio) algorithm for exploiting implicit feedback in Dense Retrievers. We demonstrate both theoretically and empirically that dense query representations learnt with CoRocchio are unbiased with respect to position bias and lead to higher retrieval effectiveness. We make available the implementations of the proposed methods and the experimental framework, along with all results at https://github.com/ielab/Counterfactual-DR.
