Papers Low Resource NMT
“Low Resource NMT” 태그가 달린 논문 34편 · 필터 해제
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation
Fine-tuning multilingual sequence-to-sequence large language models (msLLMs) has shown promise in developing neural machine translation (NMT) systems for low-resource languages (LRLs). However, conventional single-stage …
Low Resource NMTMachine TranslationNMTTransfer Learning+1From Priest to Doctor: Domain Adaptaion for Low-Resource Neural Machine Translation
Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only available parallel data are small amount…
Domain AdaptationLow Resource Neural Machine TranslationLow-Resource Neural Machine TranslationLow Resource NMT+2Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?
Monolingual data, being readily available in large quantities, has been used to upscale the scarcely available parallel data to train better models for automatic translation. Self-learning, where a model is made to learn…
Low Resource NMTNMTSelf-LearningTranslationHigh-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering
Back translation, as a technique for extending a dataset, is widely used by researchers in low-resource language translation tasks. It typically translates from the target to the source language to ensure high-quality tr…
Data AugmentationGenerative Adversarial NetworkLow Resource NMTMachine Translation+3Enhancing Low-Resource NMT with a Multilingual Encoder and Knowledge Distillation: A Case Study
Neural Machine Translation (NMT) remains a formidable challenge, especially when dealing with low-resource languages. Pre-trained sequence-to-sequence (seq2seq) multi-lingual models, such as mBART-50, have demonstrated i…
Knowledge DistillationLanguage ModelingLanguage ModellingLow Resource NMT+3Low-resource neural machine translation with morphological modeling
Morphological modeling in neural machine translation (NMT) is a promising approach to achieving open-vocabulary machine translation for morphologically-rich languages. However, existing methods such as sub-word tokenizat…
Data AugmentationDecoderLow Resource Neural Machine TranslationLow-Resource Neural Machine Translation+4Direct Neural Machine Translation with Task-level Mixture of Experts models
Direct neural machine translation (direct NMT) is a type of NMT system that translates text between two non-English languages. Direct NMT systems often face limitations due to the scarcity of parallel data between non-En…
Direct NMTLarge Language ModelLow Resource Neural Machine TranslationLow-Resource Neural Machine Translation+5Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables
Despite the tremendous success of Neural Machine Translation (NMT), its performance on low-resource language pairs still remains subpar, partly due to the limited ability to handle previously unseen inputs, i.e., general…
Low Resource Neural Machine TranslationLow-Resource Neural Machine TranslationLow Resource NMTMachine Translation+2ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods for NMT are static, which simply transfer…
Low Resource Neural Machine TranslationLow-Resource Neural Machine TranslationLow Resource NMTMachine Translation+3Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation
Training Neural Machine Translation (NMT) models suffers from sparse parallel data, in the infrequent translation scenarios towards low-resource source languages. The existing solutions primarily concentrate on the utili…
Generative Adversarial NetworkLanguage ModelingLanguage ModellingLow Resource Neural Machine Translation+8FeatureBART: Feature Based Sequence-to-Sequence Pre-Training for Low-Resource NMT
In this paper we present FeatureBART, a linguistically motivated sequence-to-sequence monolingual pre-training strategy in which syntactic features such as lemma, part-of-speech and dependency labels are incorporated int…
LEMMALow Resource NMTNMTHFT: High Frequency Tokens for Low-Resource NMT
Tokenization has been shown to impact the quality of downstream tasks, such as Neural Machine Translation (NMT), which is susceptible to out-of-vocabulary words and low frequency training data. Current state-of-the-art a…
Low Resource NMTMachine TranslationNMTSegmentation+1A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation
A significant challenge in developing translation systems for the world’s ∼7,000 languages is that very few have sufficient data for state-of-the-art techniques. Transfer learning is a promising direction for low-resourc…
Low Resource Neural Machine TranslationLow-Resource Neural Machine TranslationLow Resource NMTMachine Translation+3Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign. Our efforts were split between two front…
Domain AdaptationLow Resource NMTNMTRe-Ranking+2Machine Translation for Livonian: Catering to 20 Speakers
Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. In this paper we tackle the task of developing neural machine translation (NMT)…
Cross-Lingual TransferLow Resource NMTMachine TranslationNMT+1Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
The last decade has witnessed enormous improvements in science and technology, stimulating the growing demand for economic and cultural exchanges in various countries. Building a neural machine translation (NMT) system h…
Low Resource NMTMachine TranslationNMTRe-Ranking+2On the Effectiveness of Quasi Character-Level Models for Machine Translation
Neural Machine Translation (NMT) models often use subword-level vocabularies to deal with rare or unknown words. Although some studies have shown the effectiveness of purely character-based models, these approaches have …
Low Resource NMTMachine TranslationNMTTranslationSicilian Translator: A Recipe for Low-Resource NMT
With 17,000 pairs of Sicilian-English translated sentences, Arba Sicula developed the first neural machine translator for the Sicilian language. Using small subword vocabularies, we trained small Transformer models with …
AttributeLow-Resource Neural Machine TranslationLow Resource NMTMachine Translation+2On the Effectiveness of Quasi Character-Level Models for Machine Translation
Neural Machine Translation (NMT) models often use subword-level vocabularies to deal with rare or unknown words. Although some studies have shown the effectiveness of purely character-based models, these approaches have …
Low Resource NMTMachine TranslationNMTTranslationA Survey on Low-Resource Neural Machine Translation
Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has been conducted for neural machine transl…
Low Resource Neural Machine TranslationLow-Resource Neural Machine TranslationLow Resource NMTMachine Translation+3