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Graph Algorithms for Multiparallel Word Alignment
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; The 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03424044 ; The 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Nov 2021, Punta Cana, Dominica ; https://2021.emnlp.org/ (2021)
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ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus ...
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Abstract:
With more than 7000 languages worldwide, multilingual natural language processing (NLP) is essential both from an academic and commercial perspective. Researching typological properties of languages is fundamental for progress in multilingual NLP. Examples include assessing language similarity for effective transfer learning, injecting inductive biases into machine learning models or creating resources such as dictionaries and inflection tables. We provide ParCourE, an online tool that allows to browse a word-aligned parallel corpus, covering 1334 languages. We give evidence that this is useful for typological research. ParCourE can be set up for any parallel corpus and can thus be used for typological research on other corpora as well as for exploring their quality and properties. ... : The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2107.06632 https://dx.doi.org/10.48550/arxiv.2107.06632
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ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus ...
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SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings
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In: EMNLP 2020 ; https://hal.archives-ouvertes.fr/hal-03013194 ; EMNLP 2020, Association for Computational Linguistics, Nov 2020, Online, United States. pp.1627 - 1643 (2020)
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
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In: Findings of ACL: EMNLP 2020 (2020)
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
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