Papers with neural MT system
A Comparative Study of Extremely Low-Resource Transliteration of the World’s Languages (L18-1)
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| Challenge: | a phrase-based MT system performs better than other methods for transliterating Bible names . combining data and training a single neural system yields significant gains . |
| Approach: | They compare several machine translation methods for transliterating Bible names . they find a phrase-based MT system performs better than other methods . |
| Outcome: | The phrase-based MT system performs better than other methods, the study finds . but, the single-language system outperforms the phrase-backed MT systems . |
Alignment verification to improve NMT translation towards highly inflectional languages with limited resources (2021.eacl-main)
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| Challenge: | Existing approaches to improve translation quality using limited training data are phrase-based and syntax-based approaches. |
| Approach: | They propose to combine a neural MT system with an open source module to improve translation quality. |
| Outcome: | The proposed method improves translation quality over the best individual NMT and the standard ensemble system provided in the Marian-NMT system. |
Training Neural Machine Translation to Apply Terminology Constraints (P19-1)
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| Challenge: | Existing methods to integrate domain terminology into neural machine translation (NMT) are brittle when tested in real-world situations. |
| Approach: | They propose a method to inject custom terminology into neural machine translation at run time by using the target side of terminology entries whose source side match the input as decoding-time constraints. |
| Outcome: | The proposed method is faster than state-of-the-art decoding and more efficient than constraint-free decoding. |