提出SARAL框架，实现跨语言文档集合检索，超越传统排序列表。
Beyond Ranked Lists: The SARAL Framework for Cross-Lingual Document Set Retrieval
- 聚焦文档集合而非排序列表的跨语言检索方法
- 在六组测试中五组表现优于其他团队，覆盖三种语言
- 适合需要完整信息集合的多语言情报分析场景
机器翻译用于英语检索任意语言信息（MATERIAL）是IARPA推动的跨语言信息检索（CLIR）计划。本文详细描述了信息科学研究所（ISI）在MATERIAL项目中的跨语言摘要与领域自适应检索系统（SARAL）方案。我们提出一种新型方法，旨在检索与查询相关的文档集合，而非仅生成排序文档列表。在MATERIAL第三阶段评估中，SARAL在六组测试条件中的五组表现领先，涵盖波斯语、哈萨克语和格鲁吉亚语三种语言。
原文摘要 · Abstract (English)
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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