Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911249165451264 |
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| author | Agarwal, Shantanu Barry, Joel Boschee, Elizabeth Miller, Scott |
| author_facet | Agarwal, Shantanu Barry, Joel Boschee, Elizabeth Miller, Scott |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval Agarwal, Shantanu Barry, Joel Boschee, Elizabeth Miller, Scott Computation and Language Information Retrieval 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). |
| title | Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2511.03228 |