TL;DR
It is found that focused retrieval techniques clearly outperform document retrieval, especially for the Focused and Restricted Relevant in Context Tasks, which limit the amount of text than can be returned per topic and per article respectively.
This study empirically evaluates the robustness of an IR model to the addition of non-relevant documents by merging two collections with negligible topic overlap and finds that MDA is more effective than MDD for retrieval, whereas MDD and MDA rerankers are equally effective.
This work proposes DocPC, a document-level visual retrieval framework based on Representative Page Composition, and introduces DocViRe, a benchmark with multi-positive relevance annotations that combines multi-positive contrastive learning with sparsely scheduled listwise optimization.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
