no code implementations • WMT (EMNLP) 2020 • Yuhao Zhang, Ziyang Wang, Runzhe Cao, Binghao Wei, Weiqiao Shan, Shuhan Zhou, Abudurexiti Reheman, Tao Zhou, Xin Zeng, Laohu Wang, Yongyu Mu, Jingnan Zhang, Xiaoqian Liu, Xuanjun Zhou, Yinqiao Li, Bei Li, Tong Xiao, Jingbo Zhu
This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks.
no code implementations • IWSLT (ACL) 2022 • Yuhao Zhang, Canan Huang, Chen Xu, Xiaoqian Liu, Bei Li, Anxiang Ma, Tong Xiao, Jingbo Zhu
This paper describes NiuTrans’s submission to the IWSLT22 English-to-Chinese (En-Zh) offline speech translation task.
no code implementations • 1 Jun 2024 • Xiaoqian Liu, Guoqiang Hu, Yangfan Du, Erfeng He, Yingfeng Luo, Chen Xu, Tong Xiao, Jingbo Zhu
Simultaneous speech translation (SimulST) is a demanding task that involves generating translations in real-time while continuously processing speech input.
1 code implementation • 27 May 2024 • Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao, Junge Zhang
Decision making demands intricate interplay between perception, memory, and reasoning to discern optimal policies.
no code implementations • 29 Dec 2023 • Xiaoqian Liu, Jianbin Jiao, Junge Zhang
Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies.
no code implementations • 6 Dec 2023 • Xiaoqian Liu, Junge Zhang, Mingyi Zhang, Peipei Yang
To address these issues, we propose to integrate model cognitive capacities and evaluation metrics into a unified evaluation paradigm.
1 code implementation • 21 Sep 2023 • Chen Xu, Xiaoqian Liu, Erfeng He, Yuhao Zhang, Qianqian Dong, Tong Xiao, Jingbo Zhu, Dapeng Man, Wu Yang
In this study, we present synchronous bilingual Connectionist Temporal Classification (CTC), an innovative framework that leverages dual CTC to bridge the gaps of both modality and language in the speech translation (ST) task.
1 code implementation • 27 May 2023 • Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency.
1 code implementation • 27 May 2023 • Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu
While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies and distributing the attention weights.
no code implementations • 27 Apr 2023 • Xiaoqian Liu, Xu Han, Eric C. Chi, Boaz Nadler
In 1-bit matrix completion, the aim is to estimate an underlying low-rank matrix from a partial set of binary observations.
no code implementations • ACL (IWSLT) 2021 • Chen Xu, Xiaoqian Liu, Xiaowen Liu, Laohu Wang, Canan Huang, Tong Xiao, Jingbo Zhu
This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu sun, Bin He
Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.
no code implementations • 26 May 2020 • Jia Xue, Junxiang Chen, Ran Hu, Chen Chen, Chengda Zheng, Xiaoqian Liu, Tingshao Zhu
Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics.
no code implementations • 1 Dec 2019 • Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu sun, Bin He
Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion.
no code implementations • 10 Nov 2019 • Deli Chen, Xiaoqian Liu, Yankai Lin, Peng Li, Jie zhou, Qi Su, Xu sun
To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.