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In light of recent studies that show neural retrieval methods may intensify gender biases during retrieval, the objective of this paper is to propose a simple yet effective sampling strategy for training neural rankers that would allow... more
In light of recent studies that show neural retrieval methods may intensify gender biases during retrieval, the objective of this paper is to propose a simple yet effective sampling strategy for training neural rankers that would allow the rankers to maintain their retrieval effectiveness while reducing gender biases. Our work proposes to consider the degrees of gender bias when sampling documents to be used for training neural rankers. We report our findings on the MS MARCO collection and based on different query datasets released for this purpose in the literature. Our results show that the proposed light-weight strategy can show competitive (or even better) performance compared to the state-of-the-art neural architectures specifically designed to reduce gender biases.
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Research has shown that neural rankers can pick up and intensify gender biases. The expression of stereotypical gender biases in retrieval systems can lead to their reinforcement in users’ beliefs. As such, the objective of this paper is... more
Research has shown that neural rankers can pick up and intensify gender biases. The expression of stereotypical gender biases in retrieval systems can lead to their reinforcement in users’ beliefs. As such, the objective of this paper is to propose a bias-aware fair ranker that explicitly incorporates a notion of gender bias and hence controls how bias is expressed in documents that are retrieved. The proposed approach is designed such that it learns the notion of relevance between the document and the query from the relevant sampled documents while incorporating the notion of gender bias by penalizing irrelevant biased sampled documents. We show that unlike the state of the art, our approach reduces bias while maintaining retrieval effectiveness over different query sets.
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With emerging of structured data, retrieving entities instead of documents becomes more prevalent in order to satisfy the information need related to a query. Therefore, several high-performance entity retrieval methods have been... more
With emerging of structured data, retrieving entities instead of documents becomes more prevalent in order to satisfy the information need related to a query. Therefore, several high-performance entity retrieval methods have been introduced to the Information Retrieval (IR) community in recent years. Replicating and reproducing the standard entity retrieval methods are considered as challenging tasks in the IR community. Open-Source IR Replicability Challenge (OSIRRC) has addressed this problem by introducing a unified framework for dockerizing a variety of retrieval tasks. In this paper, a Docker image is built for six different entity retrieval models including, LM, MLM-tc, MLM-all, PRMS, SDM, FSDM. Also, Entity Linking incorporated Retrieval(ELR) extension, has been implemented that can be applied on top of all the mentioned models. The entity retrieval docker can retrieve relevant entities for any given topic. Image Source: https://github.com/osirrc/entityretrieval-docker Docker...
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file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of... more
file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) ...