Chang Li


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MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media
Chang Li | Dan Goldwasser
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

The way information is generated and disseminated has changed dramatically over the last decade. Identifying the political perspective shaping the way events are discussed in the media becomes more important due to the sharp increase in the number of news outlets and articles. Previous approaches usually only leverage linguistic information. However, news articles attempt to maintain credibility and seem impartial. Therefore, bias is introduced in subtle ways, usually by emphasizing different aspects of the story. In this paper, we propose a novel framework that considers entities mentioned in news articles and external knowledge about them, capturing the bias with respect to those entities. We explore different ways to inject entity information into the text model. Experiments show that our proposed framework achieves significant improvements over the standard text models, and is capable of identifying the difference in news narratives with different perspectives.

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Frustratingly Simple Few-Shot Slot Tagging
Jianqiang Ma | Zeyu Yan | Chang Li | Yang Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification
Chang Li | Dan Goldwasser
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media
Chang Li | Dan Goldwasser
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents’ social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.


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Structured Representation Learning for Online Debate Stance Prediction
Chang Li | Aldo Porco | Dan Goldwasser
Proceedings of the 27th International Conference on Computational Linguistics

Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users’ conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.


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PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings
I-Ta Lee | Mahak Goindani | Chang Li | Di Jin | Kristen Marie Johnson | Xiao Zhang | Maria Leonor Pacheco | Dan Goldwasser
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.


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Better Together: Combining Language and Social Interactions into a Shared Representation
Yi-Yu Lai | Chang Li | Dan Goldwasser | Jennifer Neville
Proceedings of TextGraphs-10: the Workshop on Graph-based Methods for Natural Language Processing

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Introducing DRAIL – a Step Towards Declarative Deep Relational Learning
Xiao Zhang | Maria Leonor Pacheco | Chang Li | Dan Goldwasser
Proceedings of the Workshop on Structured Prediction for NLP


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Detecting English-French Cognates Using Orthographic Edit Distance
Qiongkai Xu | Albert Chen | Chang Li
Proceedings of the Australasian Language Technology Association Workshop 2015