1 code implementation • 14 Mar 2024 • Xiuqin Zhong, Shengyuan Yan, Gongqi Lin, Hongguang Fu, Liang Xu, Siwen Jiang, Lei Huang, Wei Fang
However, adding auxiliary components automatically is challenging due to the complexity in selecting suitable auxiliary components especially when pivotal decisions have to be made.
no code implementations • 20 Oct 2021 • Yidan Hu, Yong liu, Chunyan Miao, Gongqi Lin, Yuan Miao
In this paper, we propose a novel explanation generation framework, named Hierarchical Aspect-guided explanation Generation (HAG), for explainable recommendation.
no code implementations • 25 Oct 2020 • Gongqi Lin, Yuan Miao, Xiaoyong Yang, Wenwu Ou, Lizhen Cui, Wei Guo, Chunyan Miao
To investigate machine comprehension models' ability in handling the commonsense knowledge, we created a Question and Answer Dataset with common knowledge of Synonyms (QADS).
no code implementations • 8 Sep 2019 • Yidan Hu, Gongqi Lin, Yuan Miao, Chunyan Miao
In this research, we propose a system which aims to allow computers to read articles and answer related questions with commonsense knowledge like a human being for CAT level 2.
no code implementations • 5 Sep 2019 • Yuan Miao, Gongqi Lin, Yidan Hu, Chunyan Miao
In order to be able to compare the difference between people reading and machines reading, we proposed a test called (reading) Comprehension Ability Test (CAT). CAT is similar to Turing test, passing of which means we cannot differentiate people from algorithms in term of their comprehension ability.