Search Results for author: Changzhi Sun

Found 17 papers, 7 papers with code

Pre-training Entity Relation Encoder with Intra-span and Inter-span Information

no code implementations EMNLP 2020 Yijun Wang, Changzhi Sun, Yuanbin Wu, Junchi Yan, Peng Gao, Guotong Xie

In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss.

Relation Extraction

Few Clean Instances Help Denoising Distant Supervision

1 code implementation COLING 2022 Yufang Liu, Ziyin Huang, Yijun Wang, Changzhi Sun, Man Lan, Yuanbin Wu, Xiaofeng Mou, Ding Wang

Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems.


Causal Intervention Improves Implicit Sentiment Analysis

no code implementations COLING 2022 Siyin Wang, Jie zhou, Changzhi Sun, Junjie Ye, Tao Gui, Qi Zhang, Xuanjing Huang

In this work, we propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV).

Sentiment Analysis

E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning

no code implementations Findings (ACL) 2022 Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei LI, Yanghua Xiao, Hao Zhou

Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR).

Explanation Generation Question Answering

NAIL: A Challenging Benchmark for Na\"ive Logical Reasoning

no code implementations29 Sep 2021 Xinbo Zhang, Changzhi Sun, Yue Zhang, Lei LI, Hao Zhou

Logical reasoning over natural text is an important capability towards human level intelligence.

Logical Reasoning

ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction

1 code implementation EACL 2021 Yijun Wang, Changzhi Sun, Yuanbin Wu, Hao Zhou, Lei LI, Junchi Yan

Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework.

coreference-resolution Coreference Resolution +4

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