Cheng-Te Li


2020

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GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
Yi-Ju Lu | Cheng-Te Li
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.

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Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media
Lun-Wei Ku | Cheng-Te Li
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media

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HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media
Hsin-Yu Chen | Cheng-Te Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.

2018

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Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
Lun-Wei Ku | Cheng-Te Li
Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

2017

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Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media
Lun-Wei Ku | Cheng-Te Li
Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media

2016

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Chinese Couplet Generation with Neural Network Structures
Rui Yan | Cheng-Te Li | Xiaohua Hu | Ming Zhang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media
Lun-Wei Ku | Jane Yung-jen Hsu | Cheng-Te Li
Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media

2015

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Proceedings of the third International Workshop on Natural Language Processing for Social Media
Shou-de Lin | Lun-Wei Ku | Cheng-Te Li | Erik Cambria
Proceedings of the third International Workshop on Natural Language Processing for Social Media

2011

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MemeTube: A Sentiment-based Audiovisual System for Analyzing and Displaying Microblog Messages
Cheng-Te Li | Chien-Yuan Wang | Chien-Lin Tseng | Shou-De Lin
Proceedings of the ACL-HLT 2011 System Demonstrations