Low-Resource Neural Machine Translation
23 papers with code • 1 benchmarks • 4 datasets
Low-resource machine translation is the task of machine translation on a low-resource language where large data may not be available.
Benchmarks
These leaderboards are used to track progress in Low-Resource Neural Machine Translation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ASPEC
FLoRes
KurdishInterdialect
Most implemented papers
Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task
Chinese word segmentation has entered the deep learning era which greatly reduces the hassle of feature engineering.
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT).
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences.
Sicilian Translator: A Recipe for Low-Resource NMT
ewdowiak/Sicilian_Translator
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With 17, 000 pairs of Sicilian-English translated sentences, Arba Sicula developed the first neural machine translator for the Sicilian language.
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine Translation
In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English.
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation
Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation.
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation
zanchangtong/ptvsri
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COLING 2022
Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT).
Low-resource Neural Machine Translation with Cross-modal Alignment
ictnlp/lnmt-ca
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How to achieve neural machine translation with limited parallel data?
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
nlp2ct/consisttl
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In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model.
On Bilingual Lexicon Induction with Large Language Models
Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that still, to a large extent, relies on calculating cross-lingual word representations.
