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Visual generics: How adults understand generic language with different visualizations ...
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| 82 |
Improving Multilingual Models for the Swedish Language : Exploring CrossLingual Transferability and Stereotypical Biases
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| 83 |
Effects of EFL Learning on L1 Chinese Lexis
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In: Sustainability; Volume 13; Issue 23; Pages: 13496 (2021)
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| 84 |
Automated Paraphrase Quality Assessment Using Language Models and Transfer Learning
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In: Computers; Volume 10; Issue 12; Pages: 166 (2021)
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| 85 |
Transfer Capital or Transfer Deficit: A Dual Perspective of English Learning of ESL College Transfer Students
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In: Sustainability; Volume 14; Issue 1; Pages: 214 (2021)
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| 86 |
DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering
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In: Applied Sciences; Volume 11; Issue 23; Pages: 11251 (2021)
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| 87 |
Modeling Heritage Language Phonetics and Phonology: Toward an Integrated Multilingual Sound System
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In: Languages; Volume 6; Issue 4; Pages: 209 (2021)
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| 88 |
NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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| 89 |
Heritage Tagalog Phonology and a Variationist Framework of Language Contact
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In: Languages; Volume 6; Issue 4; Pages: 201 (2021)
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| 90 |
Reusing Monolingual Pre-Trained Models by Cross-Connecting Seq2seq Models for Machine Translation
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In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
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| 91 |
Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm
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In: Mathematics; Volume 10; Issue 1; Pages: 4 (2021)
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| 92 |
Methods for Detoxification of Texts for the Russian Language
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In: Multimodal Technologies and Interaction ; Volume 5 ; Issue 9 (2021)
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| 93 |
Machine Translation in Low-Resource Languages by an Adversarial Neural Network
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In: Applied Sciences; Volume 11; Issue 22; Pages: 10860 (2021)
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Abstract:
Existing Sequence-to-Sequence (Seq2Seq) Neural Machine Translation (NMT) shows strong capability with High-Resource Languages (HRLs). However, this approach poses serious challenges when processing Low-Resource Languages (LRLs), because the model expression is limited by the training scale of parallel sentence pairs. This study utilizes adversary and transfer learning techniques to mitigate the lack of sentence pairs in LRL corpora. We propose a new Low resource, Adversarial, Cross-lingual (LAC) model for NMT. In terms of the adversary technique, LAC model consists of a generator and discriminator. The generator is a Seq2Seq model that produces the translations from source to target languages, while the discriminator measures the gap between machine and human translations. In addition, we introduce transfer learning on LAC model to help capture the features in rare resources because some languages share the same subject-verb-object grammatical structure. Rather than using the entire pretrained LAC model, we separately utilize the pretrained generator and discriminator. The pretrained discriminator exhibited better performance in all experiments. Experimental results demonstrate that the LAC model achieves higher Bilingual Evaluation Understudy (BLEU) scores and has good potential to augment LRL translations.
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Keyword:
adversarial machine learning; imbalanced datasets; machine learning; transfer learning
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URL: https://doi.org/10.3390/app112210860
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| 94 |
3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size
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In: Electronics ; Volume 10 ; Issue 17 (2021)
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| 95 |
Spanish-Catalan linguistic transfers: A study of lexical availability in Lleida ; Transferències lingüístiques castellà-català: un estudi de disponibilitat lèxica a Lleida
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In: Treballs de Sociolingüística Catalana; Núm. 31 (2021): L’estandardologia comparada: teoria i pràctica; 149-164 (2021)
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| 96 |
Держать, читать oder говорить доклад?! – Kollokationen und andere lexikalische Einheiten in der Sprachausbildung russischer Herkunftssprecher
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| 97 |
Identifying Datooga loans in Iraqw - and the other way round ...
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| 98 |
Identifying Datooga loans in Iraqw - and the other way round ...
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| 99 |
Держать, читать oder говорить доклад?! – Kollokationen und andere lexikalische Einheiten in der Sprachausbildung russischer Herkunftssprecher ...
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| 100 |
Surveying Motion Lexicalisation Patterns in L1-Portuguese/FL-English Bilinguals ...
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