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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ...
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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
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Abstract:
Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream tasks, recent increasingly popular projection-based CLE models are almost exclusively evaluated on a single task only: bilingual lexicon induction (BLI). Even BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we make the first step towards a comprehensive evaluation of cross-lingual word embeddings. We thoroughly evaluate both supervised and unsupervised CLE models on a large number of language pairs in the BLI task and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI can result in deteriorated downstream performance. We indicate the ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1902.00508 https://dx.doi.org/10.48550/arxiv.1902.00508
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Specialising Distributional Vectors of All Words for Lexical Entailment ...
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SEAGLE: A platform for comparative evaluation of semantic encoders for information retrieval
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Specializing distributional vectors of all words for lexical entailment
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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Cross-lingual semantic specialization via lexical relation induction
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Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
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SenZi: A sentiment analysis lexicon for the latinised Arabic (Arabizi)
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Informing unsupervised pretraining with external linguistic knowledge
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Do we really need fully unsupervised cross-lingual embeddings?
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Are we consistently biased? Multidimensional analysis of biases in distributional word vectors
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