Papers with domain-adaptation method
English-Indonesian Neural Machine Translation for Spoken Language Domains (P19-2)
Copied to clipboard
| 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. |
Snapshot-Guided Domain Adaptation for ELECTRA (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing domain-specific knowledge of domain-related tasks is lacking in pre-trained language models. |
| Approach: | They propose a domain-adaptation method which can dynamically select domain-specific tokens and guide the discriminator to emphasize them, without introducing new training parameters. |
| Outcome: | The proposed method can capture domain-specific knowledge of domain-related tasks without introducing new training parameters. |
Effective Use of Target-side Context for Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to train NMT systems with noisy data are not sufficient . et al., 2018) found that NMT models can learn with multiple types of corpora . |
| Approach: | They propose a Japanese-English news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus . |
| Outcome: | The proposed corpus improves translation quality and is more efficient than existing methods. |
Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for NMT (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to train NMT systems with noisy data are not sufficient . a recent increase in foreigners visiting Japan has created a significant information gap . |
| Approach: | They propose a Japanese-English parallel news corpus that is content-equivalent . they extend a domain-adaptation method to train NMT models with clean corpus . |
| Outcome: | The proposed corpus improves translation quality and is more effective than existing methods. |