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On the Copying Behaviors of Pre-Training for Neural Machine Translation ...
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Norm-Based Curriculum Learning for Neural Machine Translation ...
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Document Graph for Neural Machine Translation ...
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Abstract:
Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods only consider a few number of previous sentences. How to make use of the whole document as global contexts is still a challenge. To address this issue, we hypothesize that a document can be represented as a graph that connects relevant contexts regardless of their distances. We employ several types of relations, including adjacency, syntactic dependency, lexical consistency, and coreference, to construct the document graph. Then, we incorporate both source and target graphs into the conventional Transformer architecture with graph convolutional networks. Experiments on various NMT benchmarks, including IWSLT English--French, Chinese-English, WMT English--German and Opensubtitle English--Russian, demonstrate that using document graphs can significantly improve the translation quality. Extensive analysis verifies that the document graph is ... : Accepted by EMNLP2021 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2012.03477 https://dx.doi.org/10.48550/arxiv.2012.03477
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Shared-Private Bilingual Word Embeddings for Neural Machine Translation ...
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Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling ...
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Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation ...
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A Relationship: Word Alignment, Phrase Table, and Translation Quality
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iSentenizer-μ: Multilingual Sentence Boundary Detection Model
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Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation
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A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation
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Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus
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