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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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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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Abstract:
As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation. In this paper, we investigate how to exploit the commonality and diversity between dialects thus to build unsupervised translation models merely accessing to monolingual data. Specifically, we leverage pivot-private embedding, layer coordination, as well as parameter sharing to sufficiently model commonality and diversity among source and target, ranging from lexical, through syntactic, to semantic levels. In order to examine the effectiveness of the proposed models, we collect 20 million monolingual corpus for each of Mandarin and Cantonese, which are official language and the most widely used dialect in China. Experimental results reveal that our methods outperform rule-based simplified and traditional Chinese conversion and conventional unsupervised translation models over 12 BLEU scores. ... : AAAI 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1912.05134 https://dx.doi.org/10.48550/arxiv.1912.05134
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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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