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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Improving Machine Translation of Rare and Unseen Word Senses ...
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LexFit: Lexical Fine-Tuning of Pretrained Language Models ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. ...
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine. ...
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine ...
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.
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In: nlmid: 101531992 ; essn: 2041-1480 (2021)
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine.
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BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
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In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020)
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Multidirectional Associative Optimization of Function-Specific Word Representations ...
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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Abstract:
In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects. Moreover, they should be able to generalise the acquired world knowledge to new languages, modulo cultural differences. Advances in machine reasoning and cross-lingual transfer depend on the availability of challenging evaluation benchmarks. Motivated by both demands, we introduce Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, which includes resource-poor languages like Eastern Apurímac Quechua and Haitian Creole. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods based on multilingual pretraining and zero-shot fine-tuning falls short compared to translation-based transfer. Finally, we propose strategies to adapt multilingual models to out-of-sample ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2005.00333 https://dx.doi.org/10.48550/arxiv.2005.00333
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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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