DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages (2022.emnlp-main)
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| Challenge: | Recent advances in neural language modeling and multilingual training have prompted widespread adoption of machine translation (MT) technologies across an unprecedented range of world languages. |
| Approach: | They propose to use a dataset to assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. |
| Outcome: | The proposed model is faster than translation from scratch, but the magnitude of productivity gains varies widely across systems and languages. |
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| Challenge: | Neural Machine Translation models still require translation post-editing to rectify errors and enhance quality under critical settings. |
| Approach: | They use GPT-4 to automatically post-edit NMT outputs across several language pairs . they show that GPT4 is adept at translation post- editing, producing meaningful edits . |
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English-Basque Statistical and Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) requires large training corpora, which is problematic for low-resource languages. |
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Enhancing Large Language Models for Document-Level Translation Post-Editing Using Monolingual Data (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have excellent performance in many tasks, but they still face challenges in document translation. |
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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. |
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Neural Machine Translation Quality and Post-Editing Performance (2021.emnlp-main)
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| Challenge: | a recent study has shown that MT post-editing can reduce translation quality and speed . a large-scale study involving 30 professional translators examined the relationship between MT performance and post-edited outputs. |
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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. |
Towards Personalised and Document-level Machine Translation of Dialogue (2021.eacl-srw)
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| Challenge: | State-of-the-art (SOTA) neural machine translation systems translate texts at sentence level, ignoring context. |
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Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
| Approach: | They propose to add a tag to back-translations to help distinguish back-translated data from original parallel training data. |
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LangMark: A Multilingual Dataset for Automatic Post-Editing (2025.acl-long)
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Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler
| Challenge: | Automated post-editing (APE) aims to correct errors in machine-translated text . lack of large-scale multilingual datasets specifically tailored to NMT outputs hinders APE development . |
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CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation (2024.findings-eacl)
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| Challenge: | Neural machine translation systems exhibit limited robustness in handling source-side linguistic variations. |
| Approach: | They propose a dialectal benchmark to quantify the robustness of MT systems to handle source-side linguistic variations. |
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