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Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis
Juhua Liu, Qihuang Zhong, Liang Ding +3
Aspect-based Sentiment Analysis (ABSA) aims to determine the sentiment polarity towards an aspect. Because of the expensive and limited labelled data, the pretraining strategy has…
Improving Neural Machine Translation by Bidirectional Training
Liang Ding, Di Wu, Dacheng Tao
We present a simple and effective pretraining strategy -- bidirectional training (BiT) for neural machine translation. Specifically, we bidirectionally update the model parameters…
Semantic-Preserving Adversarial Text Attacks
Xinghao Yang, Weifeng Liu, James Bailey +2
Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification is rarely studied. Several lines of text attack methods…
The USYD-JD Speech Translation System for IWSLT 2021
Liang Ding, Di Wu, Dacheng Tao
This paper describes the University of Sydney& JD's joint submission of the IWSLT 2021 low resource speech translation task. We participated in the Swahili-English direction and go…
Progressive Multi-Granularity Training for Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence. However, recent studies show that NAT is weak at lea…
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
Liang Ding, Longyue Wang, Xuebo Liu +3
Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models. However, there exists a discrepancy on low-freque…