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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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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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20 |
Can Synthetic Translations Improve Bitext Quality? ...
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Abstract:
Synthetic translations have been used for a wide range of NLP tasks primarily as a means of data augmentation. This work explores, instead, how synthetic translations can be used to revise potentially imperfect reference translations in mined bitext. We find that synthetic samples can improve bitext quality without any additional bilingual supervision when they replace the originals based on a semantic equivalence classifier that helps mitigate NMT noise. The improved quality of the revised bitext is confirmed intrinsically via human evaluation and extrinsically through bilingual induction and MT tasks. ... : ACL 2022 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2203.07643 https://dx.doi.org/10.48550/arxiv.2203.07643
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