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Graph Neural Networks for Multiparallel Word Alignment ...
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 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2203.08654
https://dx.doi.org/10.48550/arxiv.2203.08654
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2
CaMEL: Case Marker Extraction without Labels ...
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3
Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments ...
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4
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization ...
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5
Geographic Adaptation of Pretrained Language Models ...
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6
Distributed representations for multilingual language processing
Dufter, Philipp [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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7
Domain adaptation in Natural Language Processing
Sedinkina, Marina [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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8
Combining contextualized and non-contextualized embeddings for domain adaptation and beyond
Pörner, Nina Mareike [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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9
Graph Algorithms for Multiparallel Word Alignment
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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10
Static Embeddings as Efficient Knowledge Bases? ...
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11
Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models ...
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12
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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13
Discrete and Soft Prompting for Multilingual Models ...
Zhao, Mengjie; Schütze, Hinrich. - : arXiv, 2021
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14
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus ...
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15
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models ...
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16
Wine is Not v i n. -- On the Compatibility of Tokenizations Across Languages ...
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17
Graph Algorithms for Multiparallel Word Alignment ...
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18
Locating Language-Specific Information in Contextualized Embeddings ...
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19
Dynamic Contextualized Word Embeddings ...
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20
Measuring and Improving Consistency in Pretrained Language Models ...
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