1 code implementation • CoNLL (EMNLP) 2021 • Chunhua Liu, Trevor Cohn, Lea Frermann
Humans use countless basic, shared facts about the world to efficiently navigate in their environment.
no code implementations • WS 2019 • Chunhua Liu, Dong Yu
This paper describes our system for COIN Shared Task 1: Commonsense Inference in Everyday Narrations.
no code implementations • SEMEVAL 2019 • Ruoyao Yang, Wanying Xie, Chunhua Liu, Dong Yu
Researchers have been paying increasing attention to rumour evaluation due to the rapid spread of unsubstantiated rumours on social media platforms, including SemEval 2019 task 7.
no code implementations • SEMEVAL 2019 • Wanying Xie, Mengxi Que, Ruoyao Yang, Chunhua Liu, Dong Yu
For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task.
no code implementations • 11 Jan 2019 • Yan Zhao, Lu Liu, Chunhua Liu, Ruoyao Yang, Dong Yu
We introduce a new task named Story Ending Generation (SEG), whic-h aims at generating a coherent story ending from a sequence of story plot.
no code implementations • 8 Jan 2019 • Chunhua Liu, Yan Zhao, Qingyi Si, Haiou Zhang, Bohan Li, Dong Yu
From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion.
no code implementations • PACLIC 2018 • Chunhua Liu, Haiou Zhang, Shan Jiang, Dong Yu
We divide a complete story into three narrative segments: an \textit{exposition}, a \textit{climax}, and an \textit{ending}.
1 code implementation • 8 Jan 2019 • Chunhua Liu, Shan Jiang, Hainan Yu, Dong Yu
The inference of each turn is performed on the current matching feature and the memory.
no code implementations • SEMEVAL 2018 • Meiqian Zhao, Chunhua Liu, Lu Liu, Yan Zhao, Dong Yu
To comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind.
no code implementations • SEMEVAL 2017 • Yukun Feng, Dong Yu, Jian Xu, Chunhua Liu
This paper explores the automatic learning of distributed representations of the target{'}s context for semantic frame labeling with target-based neural model.