Papers by Anxiang Ma
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Extensive experiments on 12 WMT tasks show that shallower multi-path models can achieve similar or even better performance than the deeper model. |
| Approach: | They propose to use a parameter-efficient multi-path structure to fuse features extracted from different paths to achieve better performance. |
| Outcome: | The proposed model can achieve better performance with the same number of parameters than the deeper model. |
Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)
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Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu
| Challenge: | Despite the success of speech recognition, how to encode the speech features effectively remains an open problem. |
| Approach: | They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. |
| Outcome: | The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x. |
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
Exploiting Target Language Data for Neural Machine Translation Beyond Back Translation (2024.findings-acl)
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| Challenge: | Neural Machine Translation (NMT) encounters challenges when translating in new domains and low-resource languages. |
| Approach: | They propose a variant of k-nearest neighbor machine translation that utilizes target language data by constructing a pseudo datastore. |
| Outcome: | The proposed method exhibits strong domain adaptation capability in both high-resource and low-resourced machine translation. |
On Vision Features in Multimodal Machine Translation (2022.acl-long)
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| Challenge: | Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models. |
| Approach: | They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation. |
| Outcome: | The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models. |