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

• URL: http://arxiv.org/abs/2511.03228v1
• Date: Wed, 05 Nov 2025 06:35:33 GMT
• Title: Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval
• Authors: Shantanu Agarwal, Joel Barry, Elizabeth Boschee, Scott Miller,
• Abstract summary: 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)<n>This report provides a detailed description of Information Sciences Institute's (ISI's) Summarization and domain-Adaptive Retrieval Across Language (SARAL's) effort for evaluation.<n>We outline our team's novel approach to handle CLIR with emphasis in developing an approach to retrieve a query-relevant document textitset, and not just a ranked document-list.
• Score: 5.199807441687141
• License: http://creativecommons.org/licenses/by/4.0/
• 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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