no code implementations • AACL (WAT) 2020 • Zhuoyuan Mao, Yibin Shen, Chenhui Chu, Sadao Kurohashi, Cheqing Jin
This paper describes the Japanese-Chinese Neural Machine Translation (NMT) system submitted by the joint team of Kyoto University and East China Normal University (Kyoto-U+ECNU) to WAT 2020 (Nakazawa et al., 2020).
no code implementations • 28 Feb 2023 • Guoqiang Sun, Yibin Shen, Sijin Zhou, Xiang Chen, Hongyan Liu, Chunming Wu, Chenyi Lei, Xianhui Wei, Fei Fang
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
1 code implementation • 29 Nov 2022 • Yibin Shen, Qianying Liu, Zhuoyuan Mao, Fei Cheng, Sadao Kurohashi
Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information.
1 code implementation • 21 Sep 2022 • Yibin Shen, Qianying Liu, Zhuoyuan Mao, Zhen Wan, Fei Cheng, Sadao Kurohashi
To solve Math Word Problems, human students leverage diverse reasoning logic that reaches different possible equation solutions.
1 code implementation • 27 Dec 2021 • Xiaofeng Pan, Yibin Shen, Jing Zhang, Xu He, Yang Huang, Hong Wen, Chengjun Mao, Bo Cao
In this paper, we propose a novel CTR model named MOEF for recommendations under frequent changes of occasions.
1 code implementation • COLING 2020 • Yibin Shen, Cheqing Jin
Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language.