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Shared computational principles for language processing in humans and deep language models
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In: Nat Neurosci (2022)
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Designing an Automatic Agent for Repeated Language based Persuasion Games ...
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Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification ...
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Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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Model Compression for Domain Adaptation through Causal Effect Estimation ...
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Parameter space factorization for zero-shot learning across tasks and languages ...
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Parameter space factorization for zero-shot learning across tasks and languages
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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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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CausaLM: Causal Model Explanation Through Counterfactual Language Models ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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Abstract:
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available. ... : ACL-IJCNLP 2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2012.15682 https://dx.doi.org/10.48550/arxiv.2012.15682
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ...
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The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures
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Multidirectional Associative Optimization of Function-Specific Word Representations
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Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Association for Computational Linguistics, 2020. : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020
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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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