26 citations · 31 across the 6 of their papers we have counts for
6 papers
Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation
Youngwoo Kim, Razieh Rahimi, James Allan
Most efforts in interpreting neural relevance models have focused on local explanations, which explain the relevance of a document to a query but are not useful in predicting the m…
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
Tanya Chowdhury, Razieh Rahimi, James Allan
Understanding why a model makes certain predictions is crucial when adapting it for real world decision making. LIME is a popular model-agnostic feature attribution method for the…
You can't pick your neighbors, or can you? When and how to rely on retrieval in the NN-LM
Andrew Drozdov, Shufan Wang, Razieh Rahimi +3
Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvement…
Explaining Documents' Relevance to Search Queries
Razieh Rahimi, Youngwoo Kim, Hamed Zamani +1
We present GenEx, a generative model to explain search results to users beyond just showing matches between query and document words. Adding GenEx explanations to search results gr…
Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
Zhiqi Huang, Hamed Bonab, Sheikh Muhammad Sarwar +2
Pretrained contextualized representations offer great success for many downstream tasks, including document ranking. The multilingual versions of such pretrained representations pr…
Query-driven Segment Selection for Ranking Long Documents
Youngwoo Kim, Razieh Rahimi, Hamed Bonab +1
Transformer-based rankers have shown state-of-the-art performance. However, their self-attention operation is mostly unable to process long sequences. One of the common approaches…