Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
Copied to clipboard
| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
| Outcome: | The proposed approach achieves 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system which uses multi-lingual training and back-translation. |
Similar Papers
Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
| Approach: | They propose to extend the recently introduced meta-learning algorithm for low-resource neural machine translation (NMT) they frame low-Resource translation as a meta- learning problem where we learn to adapt to low-REsource languages based on multilingual high-resourced language tasks. |
| Outcome: | The proposed meta-learning algorithm outperforms the multilingual, transfer learning based approach and can train a competitive NMT system with only a fraction of training examples. |
Low-resource Neural Machine Translation with Cross-modal Alignment (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing neural machine translation techniques rely on large monolingual corpus, which is costly for some low-resource languages. |
| Approach: | They propose a cross-modal contrastive learning method to learn a shared space for all languages by additional visual modality. |
| Outcome: | The proposed method can learn cross-modal and cross-lingual alignment with small amount of image-text pairs and achieves significant improvements over the text-only baseline. |
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
Copied to clipboard
| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
| Outcome: | The proposed approach is highly language-specific and can be tailored to the source language and its typology. |
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora (N18-4)
Copied to clipboard
| Challenge: | Recent crowdsourcing efforts and workshops on machine translation have resulted in small amounts of parallel texts for building viable machine translation systems for low resource pairs. |
| Approach: | They propose to use an end-to-end Siamese bidirectional recurrent neural network to extract parallel sentences from Wikipedia to improve BLEU scores on both NMT and phrase-based SMT systems. |
| Outcome: | The proposed approach improves BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs English–Hindi and English–Tamil when compared to training exclusively on the limited bilingual corpora. |
Few-Shot Learning Translation from New Languages (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows strong transfer learning capability to unseen languages in sequence-to-sequence neural networks . current transfer learning methods require much less downstream task data than would otherwise be required. |
| Approach: | They first train word embeddings models on varying amounts of data and plug them into a machine translation model. |
| Outcome: | The proposed model can learn Flores with only 500 parallel sentences and 31,250 sentences of monolingual data, and it can exceed 15 BLEU on unseen languages. |
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi-Task Learning Approach (N18-1)
Copied to clipboard
| Challenge: | Neural machine translation requires large amount of parallel training text to learn a reasonable quality translation model. |
| Approach: | They propose a multi-task learning approach that leverages monolingual linguistic resources in the source side of a machine translation task. |
| Outcome: | The proposed approach is effective on three translation tasks: English-to-French, English- to-Farsi, and English-à-Vietnamese. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |
Rapid Adaptation of Neural Machine Translation to New Languages (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to adapt neural machine translation systems to low-resource languages are difficult to implement and require large amounts of training data. |
| Approach: | They propose a method to train neural machine translation systems to new low-resource languages . they propose to start with massively multilingual "seed models" and continue training on data related to the LRL . |
| Outcome: | The proposed method achieves BLEU scores of up to 15.5 with no data from the LRL and improves over other adaptation methods by 1.7 BLUE points average over 4 LRL settings. |
Two Parents, One Child: Dual Transfer for Low-Resource Neural Machine Translation (2021.findings-acl)
Copied to clipboard
| Challenge: | Neural machine translation suffers when parallel data is scarce for training . a new framework to transfer multiple sources of auxiliary data is proposed . |
| Approach: | They propose a framework to transfer multiple sources of auxiliary data from high-resource parallel data to low-resourced translation models using pretrained language models. |
| Outcome: | The proposed approach yields consistent improvements over strong competitors for multiple translation directions. |
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
| Approach: | They propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related languages. |
| Outcome: | The proposed approach improves translation accuracy by 5 BLEU points over the standard Transformer-based NMT models. |