Search Results for author: Yu Wan

Found 11 papers, 4 papers with code

Challenges of Neural Machine Translation for Short Texts

no code implementations CL (ACL) 2022 Yu Wan, Baosong Yang, Derek Fai Wong, Lidia Sam Chao, Liang Yao, Haibo Zhang, Boxing Chen

After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors.

Machine Translation NMT +2

RoBLEURT Submission for WMT2021 Metrics Task

no code implementations WMT (EMNLP) 2021 Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao

After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.

Denoising

Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task

no code implementations18 Oct 2022 Keqin Bao, Yu Wan, Dayiheng Liu, Baosong Yang, Wenqiang Lei, Xiangnan He, Derek F. Wong, Jun Xie

In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation).

XLM-R

RoBLEURT Submission for the WMT2021 Metrics Task

no code implementations28 Apr 2022 Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao

After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy.

Denoising

RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation

no code implementations1 Mar 2022 Yidan Zhang, Yu Wan, Dayiheng Liu, Baosong Yang, Zhenan He

Recently, Minimum Bayes Risk (MBR) decoding has been proposed to improve the quality for NMT, which seeks for a consensus translation that is closest on average to other candidates from the n-best list.

Machine Translation NMT +1

Self-Paced Learning for Neural Machine Translation

1 code implementation EMNLP 2020 Yu Wan, Baosong Yang, Derek F. Wong, Yikai Zhou, Lidia S. Chao, Haibo Zhang, Boxing Chen

Recent studies have proven that the training of neural machine translation (NMT) can be facilitated by mimicking the learning process of humans.

Machine Translation NMT +1

Uncertainty-Aware Curriculum Learning for Neural Machine Translation

no code implementations ACL 2020 Yikai Zhou, Baosong Yang, Derek F. Wong, Yu Wan, Lidia S. Chao

We propose uncertainty-aware curriculum learning, which is motivated by the intuition that: 1) the higher the uncertainty in a translation pair, the more complex and rarer the information it contains; and 2) the end of the decline in model uncertainty indicates the completeness of current training stage.

Machine Translation NMT +1

Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling

2 code implementations11 Dec 2019 Yu Wan, Baosong Yang, Derek F. Wong, Lidia S. Chao, Haihua Du, Ben C. H. Ao

As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation.

Machine Translation Translation

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