Chao-Chun Hsu


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Decision-Focused Summarization
Chao-Chun Hsu | Chenhao Tan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Relevance in summarization is typically de- fined based on textual information alone, without incorporating insights about a particular decision. As a result, to support risk analysis of pancreatic cancer, summaries of medical notes may include irrelevant information such as a knee injury. We propose a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision. We leverage a predictive model that makes the decision based on the full text to provide valuable insights on how a decision can be inferred from text. To build a summary, we then select representative sentences that lead to similar model decisions as using the full text while accounting for textual non-redundancy. To evaluate our method (DecSum), we build a testbed where the task is to summarize the first ten reviews of a restaurant in support of predicting its future rating on Yelp. DecSum substantially outperforms text-only summarization methods and model-based explanation methods in decision faithfulness and representativeness. We further demonstrate that DecSum is the only method that enables humans to outperform random chance in predicting which restaurant will be better rated in the future.

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Answer Generation for Retrieval-based Question Answering Systems
Chao-Chun Hsu | Eric Lind | Luca Soldaini | Alessandro Moschitti
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Characterizing the Value of Information in Medical Notes
Chao-Chun Hsu | Shantanu Karnwal | Sendhil Mullainathan | Ziad Obermeyer | Chenhao Tan
Findings of the Association for Computational Linguistics: EMNLP 2020

Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes. We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize the value of information in medical notes. We show that as a whole, medical notes only provide additional predictive power over structured information in readmission prediction. We further propose a probing framework to select parts of notes that enable more accurate predictions than using all notes, despite that the selected information leads to a distribution shift from the training data (“all notes”). Finally, we demonstrate that models trained on the selected valuable information achieve even better predictive performance, with only 6.8%of all the tokens for readmission prediction.


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EmotionLines: An Emotion Corpus of Multi-Party Conversations
Chao-Chun Hsu | Sheng-Yeh Chen | Chuan-Chun Kuo | Ting-Hao Huang | Lun-Wei Ku
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues
Chao-Chun Hsu | Lun-Wei Ku
Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

This paper describes an overview of the Dialogue Emotion Recognition Challenge, EmotionX, at the Sixth SocialNLP Workshop, which recognizes the emotion of each utterance in dialogues. This challenge offers the EmotionLines dataset as the experimental materials. The EmotionLines dataset contains conversations from Friends TV show transcripts (Friends) and real chatting logs (EmotionPush), where every dialogue utterance is labeled with emotions. Organizers provide baseline results. 18 teams registered in this challenge and 5 of them submitted their results successfully. The best team achieves the unweighted accuracy 62.48 and 62.5 on EmotionPush and Friends, respectively. In this paper we present the task definition, test collection, the evaluation results of the groups that participated in this challenge, and their approach.