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Visual generics: How adults understand generic language with different visualizations ...
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Improving Multilingual Models for the Swedish Language : Exploring CrossLingual Transferability and Stereotypical Biases
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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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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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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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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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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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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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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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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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Abstract:
This work uses sequence-to-sequence (seq2seq) models pre-trained on monolingual corpora for machine translation. We pre-train two seq2seq models with monolingual corpora for the source and target languages, then combine the encoder of the source language model and the decoder of the target language model, i.e., the cross-connection. We add an intermediate layer between the pre-trained encoder and the decoder to help the mapping of each other since the modules are pre-trained completely independently. These monolingual pre-trained models can work as a multilingual pre-trained model because one model can be cross-connected with another model pre-trained on any other language, while their capacity is not affected by the number of languages. We will demonstrate that our method improves the translation performance significantly over the random baseline. Moreover, we will analyze the appropriate choice of the intermediate layer, the importance of each part of a pre-trained model, and the performance change along with the size of the bitext.
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Keyword:
natural language processing; neural machine translation; transfer learning
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URL: https://doi.org/10.3390/app11188737
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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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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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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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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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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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Держать, читать oder говорить доклад?! – Kollokationen und andere lexikalische Einheiten in der Sprachausbildung russischer Herkunftssprecher
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Identifying Datooga loans in Iraqw - and the other way round ...
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Identifying Datooga loans in Iraqw - and the other way round ...
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Держать, читать oder говорить доклад?! – Kollokationen und andere lexikalische Einheiten in der Sprachausbildung russischer Herkunftssprecher ...
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Surveying Motion Lexicalisation Patterns in L1-Portuguese/FL-English Bilinguals ...
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