| Challenge: | Neural machine translation (NMT) is an effective way to convert text to a different language without human involvement. |
| Approach: | They propose to use multihead self-attention along with pre-trained Byte-Pair-Encoded (BPE) and MultiBPE embeddings to develop an efficient machine translation system. |
| Outcome: | The proposed system outperforms Google translator and the existing translators on two of the most morphological rich Indian languages. |
Similar Papers
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| 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. |
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)
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| Challenge: | Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting . |
| Approach: | They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance. |
| Outcome: | The proposed model incorporates linguistic features to improve the translation performance. |
Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |
Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| 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. |
Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages (L18-1)
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| Challenge: | Using multilingual and multi-way neural machine translation approaches is a major advantage . training NMT systems for individual language pairs takes significantly more time than training of SMT systems . |
| Approach: | They propose to employ multilingual and multi-way neural machine translation approaches for morphologically rich languages such as Estonian and Russian. |
| Outcome: | The proposed approach improves translation quality by +3.27 BLEU points over baseline models. |
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language Bribri (2020.coling-main)
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| Challenge: | a small dataset of 5923 Bribri-Spanish pairs is used to train low-resource NMT models . |
| Approach: | They propose a Chibchan NMT model and dataset with an average performance of BLEU 16.91.7 for Bribri. |
| Outcome: | The proposed model improves on the Bribri dataset by 1.0 BLEU, but only when the new Spanish sentences belong to the same domain as the other Spanish examples. |
Byte-based Multilingual NMT for Endangered Languages (2022.coling-1)
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| Challenge: | Existing work has not studied how byte encoding can benefit endangered languages . multilingual neural machine translation (MNMT) models suffer from out-of-vocabulary issues and representation bottleneck . |
| Approach: | They propose a multilingual multilingual neural machine translation system to alleviate the representation bottleneck and improve translation performance in endangered languages. |
| Outcome: | The proposed system outperforms subword-based models on twelve languages up to +18.5 BLEU points, an 840% relative improvement over baseline models. |
Overcoming the Rare Word Problem for low-resource language pairs in Neural Machine Translation (D19-52)
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| Challenge: | Despite above approaches can improve the prediction of rare words, they still have challenges which have adverse effects on its effectiveness. |
| Approach: | They propose three ways to address rare-word problem in neural machine translation systems . they propose an algorithm to learn morphology of unknown words for English in supervised way to minimize adverse effect of rare- word problem. |
| Outcome: | The proposed approaches improve accuracy on two low-resource language pairs. |