Papers with MT paradigm
ESCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing (L18-1)
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| Challenge: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
| Approach: | a team of researchers develops a Synthetic Corpus for Automatic Post-Editing . eSCAPE is the largest freely-available Synthetic corpus for automatic post-editing released so far . the results prove that the models always improve MT quality with statistically significant gains . |
| Outcome: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
| Approach: | They propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related languages. |
| Outcome: | The proposed approach improves translation accuracy by 5 BLEU points over the standard Transformer-based NMT models. |
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. |
Benchmarking Machine Translation with Cultural Awareness (2024.findings-emnlp)
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| Challenge: | Existing studies on terminology translation focus on breaking language barriers rather than cultural barriers. |
| Approach: | They propose a parallel corpus enriched with CSI annotations in 6 language pairs for investigating Cultural-Aware Machine Translation. |
| Outcome: | The proposed corpus is enriched with CSI annotations in 6 languages and measures translation quality. |