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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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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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108 |
Towards instance-level parser selection for cross-lingual transfer of dependency parsers
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
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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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Abstract:
Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal. The lack of any supervision makes such approaches conceptually attractive. Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure. The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resource-poor and distant languages. In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings. A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero ... : EMNLP 2019 (Long paper) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1909.01638 https://arxiv.org/abs/1909.01638
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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
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Specialising Distributional Vectors of All Words for Lexical Entailment ...
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Multilingual and cross-lingual graded lexical entailment ...
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Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment ...
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Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
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Glavaš, G; Vulic, Ivan. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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Multilingual and cross-lingual graded lexical entailment
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Vulic, Ivan; Ponzetto, SP; Glavaš, G. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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Glavaš, G; Litschko, R; Ruder, S. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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