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Cross-language Information Retrieval

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arxiv 2111.05988 v2 pith:R2FSXAHY submitted 2021-11-10 cs.IR cs.AIcs.CL

Petra Galuščáková , Douglas W. Oard , Suraj Nair This is my paper · ORCID
classification cs.IRcs.AIcs.CL
keywords documentsretrievalsearcherclircross-languageinformationtheywill
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Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for CLIR and outlines some open research questions.

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  1. An Interdisciplinary Approach to Human-Centered Machine Translation

    cs.CL 2025-06 accept novelty 4.0 of 10

    A position survey calling for human-centered machine translation, synthesizing translation studies and HCI to broaden MT evaluation and design beyond benchmark quality.

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