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).
@article{arxiv.2511.03228,
title = {Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval},
author = {Shantanu Agarwal and Joel Barry and Elizabeth Boschee and Scott Miller},
journal= {arXiv preprint arXiv:2511.03228},
year = {2025}
}