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Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender ...
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MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting ...
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RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ...
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AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ...
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Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing ...
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Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing ...
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Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment ...
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ...
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ...
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Abstract:
Massively multilingual transformers pretrained with language modeling objectives (e.g., mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross-lingual transfer in NLP, offering unmatched transfer performance. Current downstream evaluations, however, verify their efficacy predominantly in transfer settings involving languages with sufficient amounts of pretraining data, and with lexically and typologically close languages. In this work, we analyze their limitations and show that cross-lingual transfer via massively multilingual transformers, much like transfer via cross-lingual word embeddings, is substantially less effective in resource-lean scenarios and for distant languages. Our experiments, encompassing three lower-level tasks (POS tagging, dependency parsing, NER), as well as two high-level semantic tasks (NLI, QA), empirically correlate transfer performance with linguistic similarity between the source and target languages, but also with the size of pretraining corpora of ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2005.00633 https://dx.doi.org/10.48550/arxiv.2005.00633
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
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Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.118, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
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Specializing unsupervised pretraining models for word-level semantic similarity
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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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From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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