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07 February 2025
 
  » arxiv » 1508.0106

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Judgment Language Matters: Multilingual Vector Space Models for Judgment Language Aware Lexical Semantics
Ira Leviant ; Roi Reichart ;
Date 1 Aug 2015
AbstractIt is a common practice in the vector space model (VSM) literature to evaluate the models’ ability to predict human similarity scores for a set of word pairs. However, existing evaluation sets, even those used to evaluate multilingual VSMs, consist of English words only. In this paper we show that this practice may have significant undesired effects on VSM evaluation. By translating the popular wordsim353 evaluation set to three languages and training state-of-the-art VSMs on corpora of the corresponding languages as well as on English, we show that: (a) The judgment language in which word pairs are presented to human evaluators, all fluent in that language, has a substantial impact on their produced similarity scores; (b) Given the judgment language of an evaluation set, this judgment language is a good choice for the VSM training corpus language; and (c) Monolingual VSMs can be combined into multilingual VSMs that can predict human similarity scores for a variety of judgment languages better than any monolingual model. Our results highlight the impact of the judgment language on the human generated similarity scores and point on the importance of constructing judgment language aware VSMs.
Source arXiv, 1508.0106
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