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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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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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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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Verb Knowledge Injection for Multilingual Event Processing ...
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Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ...
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Probing Pretrained Language Models for Lexical Semantics ...
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The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ...
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SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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Cross-lingual semantic specialization via lexical relation induction ...
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Abstract:
Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream ...
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URL: https://dx.doi.org/10.17863/cam.43734 https://www.repository.cam.ac.uk/handle/1810/296686
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ...
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