1 code implementation • ACL 2021 • Yilin Niu, Fei Huang, Jiaming Liang, Wenkai Chen, Xiaoyan Zhu, Minlie Huang
In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering.
1 code implementation • Findings (ACL) 2021 • Fangkai Jiao, Yangyang Guo, Yilin Niu, Feng Ji, Feng-Lin Li, Liqiang Nie
Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years.
1 code implementation • ACL 2020 • Yilin Niu, Fangkai Jiao, Mantong Zhou, Ting Yao, Jingfang Xu, Minlie Huang
Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor.
1 code implementation • 3 Feb 2020 • Fei Huang, Dazhen Wan, Zhihong Shao, Pei Ke, Jian Guan, Yilin Niu, Xiaoyan Zhu, Minlie Huang
In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions.
no code implementations • 25 Oct 2018 • Yilin Niu, chao qiao, Hang Li, Minlie Huang
Text similarity calculation is a fundamental problem in natural language processing and related fields.
1 code implementation • ACL 2017 • Yilin Niu, Ruobing Xie, Zhiyuan Liu, Maosong Sun
The key idea is to utilize word sememes to capture exact meanings of a word within specific contexts accurately.