MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing research has focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker’s gender. |
| Approach: | They introduce a novel neural machine translation framework that interprets all context as text. |
| Outcome: | The proposed framework outperforms a baseline that matched the parameters and significantly outperformed it in English translation. |
Similar Papers
Exploiting Sentential Context for Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to exploit sentential context for machine translation are not well studied. |
| Approach: | They propose a shallow sentential context that exploits top encoder layer, and a deep sentential one that aggregates sentential representations from all internal layers. |
| Outcome: | The proposed model outperforms the strong Transformer model on the English-German and English-French benchmarks. |
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to achieve zero-shot translation suffer from spurious correlations between output language and language invariant semantics. |
| Approach: | They propose a method that denoizes the autoencoder objective based on pivot language into traditional training objective to improve translation accuracy on zero-shot directions. |
| Outcome: | The proposed method eliminates spurious correlations and outperforms state-of-the-art methods on two benchmark machine translation datasets. |
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT (2025.acl-short)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but still struggles with word ambiguity and context. |
| Approach: | They create a new Czech-to-polish e-commerce product translation dataset coupled with images and product metadata consisting of 11,400 sentence pairs. |
| Outcome: | The proposed model incorporates visual cues alongside textual data to improve translation quality. |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
Language Tags Matter for Zero-Shot Neural Machine Translation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on multilingual machine translation have ignored the importance of LTs. |
| Approach: | They propose to use language tag (LT) strategies to indicate translation directions in MNMT to enhance consistency and alleviate off-target issues in zero-shot directions. |
| Outcome: | The proposed model could translate between unsupervised languages and achieve a +8 BLEU score difference over other LT strategies in translation tasks. |
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
Copied to clipboard
| Challenge: | Recent studies have shown that multilingual NMT models can handle more than one translation direction with a single system. |
| Approach: | They propose a multilingual neural machine translation model that can handle more than one translation direction with a single system. |
| Outcome: | The proposed model performs well in low-resource settings against bilingual systems. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
LLMs Are Zero-Shot Context-Aware Simultaneous Translators (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing SiMT systems operate on a sentence level, disregarding the context established by previous sentences or the broader context implied by previous words. |
| Approach: | They show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
| Outcome: | The proposed models perform on par with or better than state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts. (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Neural Machine Translation systems are prone to gender biases in their learned representations. |
| Approach: | They propose to use contextual sentences to correct gender bias in Neural Machine Translation models. |
| Outcome: | The proposed method can be used to build better, bias-free translation systems. |
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu (2025.acl-long)
Copied to clipboard
| Challenge: | In-context machine translation (MT) with large language models can take advantage of linguistic resources such as grammar books and dictionaries. |
| Approach: | They propose to use in-context machine translation (MT) with large language models to take advantage of linguistic resources such as grammar books and dictionaries. |
| Outcome: | The proposed approach can take advantage of dictionaries and grammar books, but its performance is poor for many lowresource languages. |