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1
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
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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2
Multidirectional Associative Optimization of Function-Specific Word Representations ...
Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Apollo - University of Cambridge Repository, 2020
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3
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ...
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4
Emergent Communication Pretraining for Few-Shot Machine Translation ...
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5
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
Majewska, Olga; Vulic, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2020
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6
Emergent Communication Pretraining for Few-Shot Machine Translation ...
Li, Yaoyiran; Ponti, Edoardo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2020
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7
Emergent Communication Pretraining for Few-Shot Machine Translation ...
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8
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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9
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2012.15682
https://dx.doi.org/10.48550/arxiv.2012.15682
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10
Verb Knowledge Injection for Multilingual Event Processing ...
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11
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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12
Probing Pretrained Language Models for Lexical Semantics ...
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13
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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14
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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15
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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16
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020
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17
Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ...
Karan, Mladen; Vulic, Ivan; Korhonen, Anna. - : Apollo - University of Cambridge Repository, 2020
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18
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ...
Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : Apollo - University of Cambridge Repository, 2020
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19
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces
Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : 5TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2020), 2020
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20
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
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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