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Graph Neural Networks for Multiparallel Word Alignment ...
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Abstract:
After a period of decrease, interest in word alignments is increasing again for their usefulness in domains such as typological research, cross-lingual annotation projection, and machine translation. Generally, alignment algorithms only use bitext and do not make use of the fact that many parallel corpora are multiparallel. Here, we compute high-quality word alignments between multiple language pairs by considering all language pairs together. First, we create a multiparallel word alignment graph, joining all bilingual word alignment pairs in one graph. Next, we use graph neural networks (GNNs) to exploit the graph structure. Our GNN approach (i) utilizes information about the meaning, position, and language of the input words, (ii) incorporates information from multiple parallel sentences, (iii) adds and removes edges from the initial alignments, and (iv) yields a prediction model that can generalize beyond the training sentences. We show that community detection provides valuable information for ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2203.08654 https://dx.doi.org/10.48550/arxiv.2203.08654
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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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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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The Effect of Mediation via the Interventionist Model of Dynamic Assessment on Reading Comprehension: Evidence from Iranian EFL Learners
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In: Journal of Education and Practice; Vol 8, No 35 (2017); 169-180 (2018)
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Visual and Spoken Texts in MCALL Courseware: The Effects of Text Modalities on the Vocabulary Retention of EFL Learners
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In: English Language Teaching; Vol 3, No 2 (2010); P30 (2010)
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