| Challenge: | Several phenomena where asymmetry arises have been identified as challenging problems for machine translation. |
| Approach: | They perform a fine-grained analysis of how an SMT system compares with two NMT systems when translating bare nouns into English. |
| Outcome: | The proposed model outperforms the SMT and BiLSTM models for 4 categories and the BiLST outperformed the SLT models for 3 categories. |
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Investigating Failures of Automatic Translation
in the Case of Unambiguous Gender (2022.acl-long)
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| Challenge: | Existing models are unable to make basic deductions regarding how to correctly inflect nouns with grammatical gender. |
| Approach: | They propose to evaluate NMT models' ability to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. |
| Outcome: | The proposed model was unable to translate gender morphology correctly in unambiguous contexts across syntactically diverse sentences. |
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. |
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)
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| Challenge: | Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting . |
| Approach: | They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance. |
| Outcome: | The proposed model incorporates linguistic features to improve the translation performance. |
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. |
A Comparison between Pre-training and Large-scale Back-translation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | BERT is a promising technique to improve NMT, but how it outperforms standard NMT is understudied. |
| Approach: | We compare MT engines trained with pre-trained BERT and back-translation with incrementally larger amounts of data. |
| Outcome: | The proposed technique outperforms standard NMT models on morphology and syntax. |
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation (D18-1)
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| Challenge: | Neural sequence-to-sequence models have proven effective for machine translation, but at the expense of interpretability. |
| Approach: | They analyze how morphological features are captured at different levels of the NMT encoder while varying the target language. |
| Outcome: | The proposed model is not interpretable, but only captures morphological features in context and only to the extent they are directly transferable to the target words. |
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. |
| Outcome: | The proposed systems outperform OpenNMT, Moses SMT and Google Translate in English-Basque translation. |
English-Indonesian Neural Machine Translation for Spoken Language Domains (P19-2)
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| Challenge: | Neural machine translation (NMT) is a data-driven method that requires a large amount of data to build a robust model. |
| Approach: | They conduct a study on Neural Machine Translation (NMT) for English-Indonesian and Indonesian-English (ID-EN) they build NMT systems using the Transformer model for both translation directions and implement domain adaptation method to train pre-trained NMT on speech language data. |
| Outcome: | The proposed model can learn formal translation outputs for English-Indonesian and Indonesian-English (ID-EN) given a small dataset of speech-styled language and a larger dataset of less formal language, the proposed model will be useful for learning formality level. |
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
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. |