DRA: dynamic routing attention for neural machine translation with low-resource languages

Wang, Zhenhan; Song, Ran; Yu, Zhengtao; Mao, Cunli; Gao, Shengxiang

doi:10.1007/s13042-024-02206-3

Int. J. Mach. Learn. & Cyber.

2024

DOI: 10.1007/s13042-024-02206-3

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DRA: dynamic routing attention for neural machine translation with low-resource languages

Zhenhan Wang,

Ran Song,

Zhengtao Yu

et al.

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Cited by 2 publications

References 31 publications

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Based on Gated Dynamic Encoding Optimization, the LGE-Transformer Method for Low-Resource Neural Machine Translation

Xu,

Zhan,

Yang

et al. 2024

IEEE Access

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Based on Gated Dynamic Encoding Optimization, the LGE-Transformer Method for Low-Resource Neural Machine Translation

Xu,

Zhan,

Yang

et al. 2024

IEEE Access

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Augmenting Low-Resource Cross-Lingual Summarization with Progression-Grounded Training and Prompting

Ma,

Huang,

Wang

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Cross-lingual summarization (CLS) , generating summaries in one language from source documents in another language, offers invaluable assistance in enabling global access to information for people worldwide. State-of-the-art neural summarization models typically train or fine-tune language models on large-scale corpora. However, this is difficult to achieve in realistic low-resource scenarios due to the lack of domain-specific annotated data. In this paper, we present a novel cross-lingual summarization model that utilizes progressive training with mBART and employs reinforcement learning to optimize discrete prompts, which addresses low-resource cross-lingual summarization through a two-pronged approach. During training, we introduce a progressive approach based on mBART, which allows the pre-trained model to gradually acquire the ability to compress information, develop cross-lingual capabilities, and ultimately adapt to specific summarization tasks. During downstream summarization, we employ a discrete-prompts joint pre-trained model based on reinforcement learning optimization to achieve low-resource cross-lingual summarization. Experimental results on four cross-lingual summarization datasets demonstrate state-of-the-art performance and superiority compared to six baselines in low-resource scenarios.

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DRA: dynamic routing attention for neural machine translation with low-resource languages

Cited by 2 publications

References 31 publications

Based on Gated Dynamic Encoding Optimization, the LGE-Transformer Method for Low-Resource Neural Machine Translation

Based on Gated Dynamic Encoding Optimization, the LGE-Transformer Method for Low-Resource Neural Machine Translation

Augmenting Low-Resource Cross-Lingual Summarization with Progression-Grounded Training and Prompting

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