Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval

November 05, 2025 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Shantanu Agarwal, Joel Barry, Elizabeth Boschee, Scott Miller arXiv ID 2511.03228 Category cs.CL: Computation & Language Cross-listed cs.IR Citations 0 Venue arXiv.org Last Checked 6 months ago
Abstract
Machine Translation for English Retrieval of Information in Any Language (MATERIAL) is an IARPA initiative targeted to advance the state of cross-lingual information retrieval (CLIR). This report provides a detailed description of Information Sciences Institute's (ISI's) Summarization and domain-Adaptive Retrieval Across Language's (SARAL's) effort for MATERIAL. Specifically, we outline our team's novel approach to handle CLIR with emphasis in developing an approach amenable to retrieve a query-relevant document \textit{set}, and not just a ranked document-list. In MATERIAL's Phase-3 evaluations, SARAL exceeded the performance of other teams in five out of six evaluation conditions spanning three different languages (Farsi, Kazakh, and Georgian).
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