Abstract
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.
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Rekabsaz, N., Lupu, M. (2014). A Real-World Framework for Translator as Expert Retrieval. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_14
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DOI: https://doi.org/10.1007/978-3-319-11382-1_14
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