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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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SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment
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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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A neural classification method for supporting the creation of BioVerbNet ...
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A neural classification method for supporting the creation of BioVerbNet ...
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
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A neural classification method for supporting the creation of BioVerbNet ...
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Second-order contexts from lexical substitutes for few-shot learning of word representations ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet ...
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Enhancing biomedical word embeddings by retrofitting to verb clusters ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet
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Second-order contexts from lexical substitutes for few-shot learning of word representations
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
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A neural classification method for supporting the creation of BioVerbNet
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