Papers by Zewei Sun
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation (2023.findings-acl)
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| Challenge: | Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. |
| Approach: | They propose a framework for multimodal machine translation that utilizes large-scale non-triple data and a multimodal translation dataset. |
| Outcome: | The proposed method can significantly improve translation performance with more non-triple data. |
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation (2023.findings-acl)
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Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu, Zewei Sun, Shanbo Cheng, Mingxuan Wang, Degen Huang, Jinsong Su
| Challenge: | Existing datasets focus on captions describing images or videos, which are not large and diverse enough. |
| Approach: | They propose a large-scale video subtitle translation dataset to facilitate multi-modality machine translation. |
| Outcome: | The proposed dataset is 10 times larger than the widely used *How2* and *VaTeX* datasets. |
Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies show that the attention heads in Transformer are not equal. |
| Approach: | They propose a masking method to mask attention heads in Transformer . they empirically validate the inequality and propose 'head mask' method to avoid bottleneck . |
| Outcome: | The proposed masking method improves translation performance on multiple languages . it can be used to remove a small subset of heads without affecting performance . |
Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
| Approach: | They propose to use the original Transformer model to test document-level neural machine translation . they find that the original transformer models can achieve strong results for document translation if trained properly . |
| Outcome: | The proposed model outperforms sentence-level models on nine datasets and two sentence- level datasets across six languages. |
Controlling Styles in Neural Machine Translation with Activation Prompt (2023.findings-acl)
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| Challenge: | Earlier studies on controlling styles in neural machine translation (NMT) have focused on regulating the level of formality, but they still encounter two major challenges. |
| Approach: | They propose a method to control the style of neural machine translation by retrieving prompts from stylized monolingual corpus. |
| Outcome: | The proposed method can control the style of translation and achieve remarkable performance. |
Multilingual Translation via Grafting Pre-trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing methods to graft pre-trained (masked) language models to multilingual data are limited, and they lack cross-attention component. |
| Approach: | They propose to graft separately pre-trained (masked) language models for machine translation using monolingual data and parallel data. |
| Outcome: | The proposed method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLUE in en2x directions compared with the multilingual Transformer of the same size. |