A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch (L18-1)
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| Challenge: | Since 2016, the landscape of automated translation has substantially changed with the arrival of neural machine translation (NMT). |
| Approach: | They propose to use an annotated SCATE corpus of MT errors to enrich the SCATE error taxonomy to fit the neural MT output. |
| Outcome: | The proposed system outperforms phrase-based and rule-based systems except for lexical issues. |
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NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations (2020.lrec-1)
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| Challenge: | Recent work proposes neural machine translation (NMT) for Brazilian Portuguese. |
| Approach: | They propose a neural machine translation approach that generates equivalent sentences in target language and source language. |
| Outcome: | The proposed approach outperforms phrase-based statistical machine translation systems for some pairs of languages. |
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. |
| Approach: | They propose to use an open-domain and an IT-domain corpora to train machine translations in English-Basque. |
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A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
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| 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. |
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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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. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
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Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach (D19-1)
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| Challenge: | Current machine translation techniques are bottlenecked by adequacy issues . we propose automatic detection of missing and wrong translations . |
| Approach: | They propose automatic detection of adequacy errors in MT hypothesis for MT model evaluation by annotating missing and wrong translations in 15000 Chinese-English translation pairs. |
| Outcome: | The proposed model can detect missing and wrong translations in 15000 Chinese-English translation pairs. |
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. |
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Refined Assessment for Translation Evaluation: Rethinking Machine Translation Evaluation in the Era of Human-Level Systems (2025.findings-emnlp)
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Dmitry Popov, Vladislav Negodin, Ekaterina Enikeeva, Iana Matrosova, Nikolay Karpachev, Max Ryabinin
| Challenge: | Currently, traditional evaluation methods struggle to detect subtle translation errors. |
| Approach: | They propose to use a dataset of human evaluations for English–Russian translations created by professional linguists to enable consistent and rich annotation. |
| Outcome: | The proposed protocol allows expert assessments without time pressure to yield substantially different results from standard evaluations. |
Analysing Coreference in Transformer Outputs (D19-65)
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| Challenge: | Using a transformer architecture, we study coreference phenomena in three neural machine translation systems. |
| Approach: | They analyse coreference phenomena in three neural machine translation systems . they manually annotate (the possibly incorrect) coreference chains in the outputs . |
| Outcome: | The proposed model shows stronger translationese effects in machine translated outputs than in human translations. |