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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
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42 |
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
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43 |
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages
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Glavas, Goran; Karan, Mladen; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.559, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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Specializing unsupervised pretraining models for word-level semantic similarity
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Non-linear instance-based cross-lingual mapping for non-isomorphic embedding spaces
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Classification-based self-learning for weakly supervised bilingual lexicon induction
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AraWEAT: Multidimensional analysis of biases in Arabic word embeddings
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Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers
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XHate-999: analyzing and detecting abusive language across domains and languages
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
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XCOPA: A multilingual dataset for causal commonsense reasoning
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Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces
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From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers
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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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56 |
Towards instance-level parser selection for cross-lingual transfer of dependency parsers
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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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