Papers by Cheng-Te Li
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media (2020.emnlp-main)
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| Challenge: | Existing methods for detecting cyberbullying rely on text analysis of social media sessions. |
| Approach: | They propose a deep model that uses a comment encoder and a post-comment co-attention sub-network to explain why a media session is identified as cyberbullying. |
| Outcome: | The proposed model outperforms existing models on real datasets and shows evidential comments in the model explainability of cyberbullying detection. |
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020.acl-main)
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| Challenge: | Existing methods to detect fake news on social media are based on textual features and advanced linguistic features. |
| Approach: | They propose a neural network-based model to detect fake news on social media . they use a short-text tweet and a sequence of retweets without text comments to predict whether the source tweet is fake or not. |
| Outcome: | The proposed model outperforms state-of-the-art methods by 16% on real tweet datasets and produces reasonable explanations. |
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (2021.naacl-main)
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| Challenge: | Existing methods to relation extraction require labeled data, but labeling is difficult . Existing models cannot recognize rare instances that are never covered by training data . |
| Approach: | They propose a multi-task learning model that directly predicts unseen relations without hand-crafted attribute labeling and multiple pairwise classifications. |
| Outcome: | The proposed model outperforms existing methods by 13.54% on two well-known datasets. |