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Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice ...
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Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang ...
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Similarity between person roles in a card sorting experiment ...
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SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations ...
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Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate ...
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Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation ...
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Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean ...
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A comparative study of several parameterizations for speaker recognition ...
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A Neural Pairwise Ranking Model for Readability Assessment ...
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A bilingual approach to specialised adjectives through word embeddings in the karstology domain ...
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Speaker verification in mismatch training and testing conditions ...
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Universal Conditional Masked Language Pre-training for Neural Machine Translation ...
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SMDT: Selective Memory-Augmented Neural Document Translation ...
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Learning How to Translate North Korean through South Korean ...
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Abstract:
South and North Korea both use the Korean language. However, Korean NLP research has focused on South Korean only, and existing NLP systems of the Korean language, such as neural machine translation (NMT) models, cannot properly handle North Korean inputs. Training a model using North Korean data is the most straightforward approach to solving this problem, but there is insufficient data to train NMT models. In this study, we create data for North Korean NMT models using a comparable corpus. First, we manually create evaluation data for automatic alignment and machine translation. Then, we investigate automatic alignment methods suitable for North Korean. Finally, we verify that a model trained by North Korean bilingual data without human annotation can significantly boost North Korean translation accuracy compared to existing South Korean models in zero-shot settings. ... : 8 pages, 1 figures, 8 tables ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2201.11258 https://arxiv.org/abs/2201.11258
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When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation? ...
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Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation ...
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