| Challenge: | Neural machine translation (NMT) methods relied on the availability of high-quality parallel corpora. |
| Approach: | They propose a method where an emergent NMT system is used for selecting training data and learning internal NMT representations. |
| Outcome: | The proposed method achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training. |
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Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020.acl-main)
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Aditya Siddhant, Ankur Bapna, Yuan Cao, Orhan Firat, Mia Chen, Sneha Kudugunta, Naveen Arivazhagan, Yonghui Wu
| Challenge: | Existing multilingual NMT approaches do not utilize the abundance of monolingual data, especially in low-resource languages. |
| Approach: | They propose to combine monolingual data with self-supervision to pre-train translation models and fine-tune on small amounts of supervised data. |
| Outcome: | The proposed approach improves translation quality of low-resource languages and zero-shot translation quality. |
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
An Effective Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only. |
| Approach: | They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems. |
| Outcome: | The proposed system achieves 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more in the (supervised) shared task winner back in 2014. |
Self-Paced Learning for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing studies have shown that the training of neural machine translation (NMT) rely on the quality of artificial schedule drawn up with the handcrafted features, e.g. sentence length or word rarity. |
| Approach: | They propose to train NMT model using a self-paced learning approach that allows it to quantify the learning confidence over training examples and flexibly govern its learning via regulating the loss in each iteration step. |
| Outcome: | The proposed model outperforms baseline models and those trained with human-designed curricula on translation quality and convergence speed. |
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. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination (D18-1)
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| Challenge: | Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. |
| Approach: | They propose to use mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. |
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On-the-Fly Fusion of Large Language Models and Machine Translation (2024.findings-naacl)
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| Challenge: | a weaker-at-translation LLM can improve translations of a NMT model, compared to a strong dedicated model. |
| Approach: | They propose to ensemble a neural machine translation model with a large language model, prompted on the same task and input. |
| Outcome: | The proposed method can be combined with various techniques from LLM prompting, such as in context learning and translation context. |
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation (P18-2)
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| Challenge: | Existing methods to train neural machine translation (NMT) use a fixed training procedure where each sentence is sampled once during each epoch. |
| Approach: | They propose to dynamically sample sentences to accelerate NMT training . a weight is assigned to each sentence based on the measured difference between training costs of two iterations. |
| Outcome: | Empirical results show that the proposed method can significantly accelerate training and improve NMT performance. |