Papers with language-specific components
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020.acl-main)
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| Challenge: | Existing approaches to improve multilingual neural machine translation (NMT) are weak, and lack robustness to support language pairs with varying typological characteristics. |
| Approach: | They propose to deepen NMT models to support language pairs with varying typological characteristics by random online backtranslation. |
| Outcome: | The proposed approach narrows the performance gap with bilingual models and improves zero-shot performance by 10 BLEU, approaching conventional pivot-based methods. |
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling (C18-1)
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| Challenge: | Existing studies on multitask and multilingual joint learning focus on cross-task or cross-lingual knowledge transfer. |
| Approach: | They propose to divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and different languages. |
| Outcome: | The proposed method is able to support multi-task and multi-lingual learning using Japanese and English data sets with three different lexical utterance classification tasks. |