A Real-World Framework for Translator as Expert Retrieval

N. Rekabsaz, M. Lupu:
"A Real-World Framework for Translator as Expert Retrieval";
Vortrag: Conference and Labs of the Evaluation Forum, CLEF 2014, Sheffield, UK; 15.09.2014 - 18.09.2014; in:"Information Access Evaluation. Multilinguality, Multimodality, and Interaction", Springer, LNCS 8685 (2014), ISBN: 978-3-319-11381-4; S. 141 - 152.

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This article describes a method and tool to identify expert translators in an on-demand translation service. We start from existing efforts on expert retrieval and factor in additional parameters based on the real-world scenario of the task. The system first identifies topical expertise using an aggregation function over relevance scores of previously translated documents by each translator, and then a learning to rank method to factor in non-topical relevance factors that are part of the decision-making process of the user, such as price and duration of translation. We test the system on a manually created test collection and show that the method is able to effectively support the user in selecting the best translator.