| Challenge: | Neural machine translation (NMT) is a well-known and expensive task. |
| Approach: | They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext. |
| Outcome: | The proposed approach achieves state-of-the-art results on WMT16, WMT17, WTM18 EnglishGerman translations and WTM19 GermanFrench translations. |
Similar Papers
Understanding Back-Translation at Scale (D18-1)
Copied to clipboard
| Challenge: | An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. |
| Approach: | They propose to augment parallel training corpus with back-translations of target language sentences to improve neural machine translation with monolingual data. |
| Outcome: | The proposed method achieves a state-of-the-art of 35 BLEU on the WMT’14 English-German test set. |
AugVic: Exploiting BiText Vicinity for Low-Resource NMT (2021.findings-acl)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) systems often exhibit poor performance due to the lack of large bitext training corpora in low-resource languages. |
| Approach: | They propose a data augmentation framework which exploits the vicinal samples of the given bitext without using extra monolingual data explicitly. |
| Outcome: | The proposed framework can diversify in-domain bitext data with finer level control on four low-resource language pairs. |
Can Synthetic Translations Improve Bitext Quality? (2022.acl-long)
Copied to clipboard
| Challenge: | Synthetic translations have been used for a wide range of NLP tasks, but it remains unclear how they differ from naturally occurring data. |
| Approach: | They propose to use a semantic equivalence classifier to improve bitext quality without additional bilingual supervision to replace the originals. |
| Outcome: | The proposed samples improve bitext quality without additional bilingual supervision and are validated intrinsically and extrinsically through bilingual induction and MT tasks. |
Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to improve Neural Machine Translation (NMT) for lowresource languages are often trained on heuristically aligned or automatically mined data. |
| Approach: | They propose to filter out imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT) instead, they propose to refine mined bitexts by automatic editing . |
| Outcome: | The proposed method improves the quality of mined bitexts for low-resource languages by up to 8 BLEU points. |
Enhancing Neural Machine Translation Through Target Language Data: A kNN-LM Approach for Domain Adaptation (2025.acl-long)
Copied to clipboard
Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu
| Challenge: | Neural machine translation (NMT) has made significant progress in recent years, yet often suffers from translating in new domains, which is called domain adaptation. |
| Approach: | They propose a method that leverages semantically similar target language sentences in the kNN framework and generates a probability distribution over these sentences during decoding. |
| Outcome: | The proposed method generates a probability distribution over similar target language sentences and then interpolates with the model’s distribution. |
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020.acl-main)
Copied to clipboard
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. |
Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation. |
| Approach: | They propose a method that leverages the capabilities of Large Language Models to optimize document translation using only monolingual data. |
| Outcome: | The proposed method improves translation quality and improves contextual consistency in document translation using only monolingual data. |
Exploiting Target Language Data for Neural Machine Translation Beyond Back Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages. |
| Approach: | They propose a variant of k-nearest neighbor machine translation that utilizes target language data by constructing a pseudo datastore. |
| Outcome: | The proposed method exhibits strong domain adaptation capability in both high-resource and low-resourced machine translation. |
Unsupervised Extraction of Partial Translations for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural machine translation systems usually require a large quantity of bilingual parallel data for training. |
| Approach: | They propose an algorithm for extracting from monolingual data what they call partial translations . partial translation is a pair of source and target sentences that contain sequences of tokens that are translations of each other. |
| Outcome: | The proposed algorithm extracts from monolingual data what we call partial translations . it takes only source and target monolingual datasets as input . |