| Challenge: | Pretrained word embeddings improve multimodal machine translation of low-resource domains due to a shortage of training data. |
| Approach: | They propose to combine pretrained word embeddings with search-based approaches to improve NMT of low-resource domains to better translate rare words. |
| Outcome: | The proposed approach improves translation performance by 1.24 METEOR and 2.49 BLEU and achieves 7.67 F-score. |
Similar Papers
When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation? (N18-2)
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| Challenge: | Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks where large-scale parallel corpora cannot be obtained. |
| Approach: | They perform five sets of experiments to analyze when pre-trained word embeddings can be useful in NMT tasks. |
| Outcome: | The embeddings provide gains of up to 20 BLEU points in the most favorable setting. |
A Simple and Fast Strategy for Handling Rare Words in Neural Machine Translation (2022.aacl-srw)
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| Challenge: | Neural Machine Translation (NMT) has been gaining popularity due to its ability to bias in highfrequency words, low-frequency words have little chance of being considered in the inference process. |
| Approach: | They propose a strategy for integrating constraints during the training and decoding process to improve the translation of rare words. |
| Outcome: | The proposed approach improves translation of rare words in high and low-resource translation tasks, showing improvements of up to +1.8 BLEU scores over baseline systems. |
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. |
Multilingual Neural Machine Translation with Language Clustering (D19-1)
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| Challenge: | Existing work on multilingual neural machine translation has been neglected due to its burdensome training process. |
| Approach: | They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. |
| Outcome: | The proposed model reduces the cost of training and improves translation accuracy. |
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. |
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. |
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020.acl-main)
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| Challenge: | Existing approaches to improve multilingual neural machine translation (NMT) are weak, and lack robustness to support language pairs with varying typological characteristics. |
| Approach: | They propose to deepen NMT models to support language pairs with varying typological characteristics by random online backtranslation. |
| Outcome: | The proposed approach narrows the performance gap with bilingual models and improves zero-shot performance by 10 BLEU, approaching conventional pivot-based methods. |
Neural Machine Translation for Low-Resourced Indian Languages (2020.lrec-1)
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| 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. |
Unsupervised Statistical Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
| Approach: | They propose a phrase-based approach that trains from monolingual corpora . their method is based on phrase-driven Statistical Machine Translation (SMT) they propose to train NMT systems without supervision from monolinguistic corpors . |
| Outcome: | The proposed approach improves on the existing supervised systems by combining a phrase table with an n-gram language model and fine-tuning hyperparameters through an unsupervised MERT variant. |