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Multi-Adversarial Learning for Cross-Lingual Word Embeddings ...
NAACL 2021 2021; Henderson, James; Merlo, Paola; Wang, Haozhou. - : Underline Science Inc., 2021
Abstract: Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.39/ Abstract: Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings -maps of matching words across languages- without supervision. Despite these successes, GANs' performance for the difficult case of distant languages is still not satisfactory. These limitations have been explained by GANs' incorrect assumption that source and target embedding spaces are related by a single linear mapping and are approximately isomorphic. We assume instead that, especially across distant languages, the mapping is only piece-wise linear, and propose a multi-adversarial learning method. This novel method induces the seed cross-lingual dictionary through multiple mappings, each induced to fit the mapping for one subspace. Our experiments on unsupervised bilingual lexicon induction and cross-lingual document classification show that this method improves performance over previous single-mapping methods, ...
Keyword: Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing
URL: https://underline.io/lecture/19831-multi-adversarial-learning-for-cross-lingual-word-embeddings
https://dx.doi.org/10.48448/0zm8-zc95
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2
Multi-Adversarial Learning for Cross-Lingual Word Embeddings ...
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3
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings
In: http://infoscience.epfl.ch/record/275419 (2020)
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4
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings ...
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5
CoNLL 2017 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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6
CLCL (Geneva) DINN Parser : a Neural Network Dependency Parser Ten Years Later
In: Proceedings of the CoNLL 2017 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies P. 228–236 (2017)
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