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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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Emergent Communication Pretraining for Few-Shot Machine Translation ...
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Abstract:
While state-of-the-art models that rely upon massively multilingual pretrained encoders achieve sample efficiency in downstream applications, they still require abundant amounts of unlabelled text. Nevertheless, most of the world's languages lack such resources. Hence, we investigate a more radical form of unsupervised knowledge transfer in the absence of linguistic data. In particular, for the first time we pretrain neural networks via emergent communication from referential games. Our key assumption is that grounding communication on images---as a crude approximation of real-world environments---inductively biases the model towards learning natural languages. On the one hand, we show that this substantially benefits machine translation in few-shot settings. On the other hand, this also provides an extrinsic evaluation protocol to probe the properties of emergent languages ex vitro. Intuitively, the closer they are to natural languages, the higher the gains from pretraining on them should be. For instance, ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2011.00890 https://arxiv.org/abs/2011.00890
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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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