Papers with DRMM
Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
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| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
A Counterfactual Explanation Framework for Retrieval Models (2026.findings-acl)
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| Challenge: | Existing literature on explainability of information retrieval has focused on illustrating the concept of relevance concerning a retrieval model. |
| Approach: | They propose to add terms to a document to improve its ranking to answer the question of which words played a role in not being favored by a retrieval model. |
| Outcome: | The proposed framework predicts counterfactuals for statistical and deep-learning models. |