The Limitations of Cross-language Word Embeddings Evaluation

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Abstract

The aim of this work is to explore the possible limitations of existing methods of cross-language word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods. To prove this hypothesis, we construct English-Russian datasets for extrinsic and intrinsic evaluation tasks and compare performances of 5 different cross-language models on them. The results say that the scores even on different intrinsic benchmarks do not correlate to each other. We can conclude that the use of human references as ground truth for cross-language word embeddings is not proper unless one does not understand how do native speakers process semantics in their cognition.

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Bakarov, A., Suvorov, R., & Sochenkov, I. (2018). The Limitations of Cross-language Word Embeddings Evaluation. In NAACL HLT 2018 - Lexical and Computational Semantics, SEM 2018, Proceedings of the 7th Conference (pp. 94–100). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S18-2010

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