Papers by Francine Chen
Empathy Prediction from Diverse Perspectives (2025.acl-long)
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Francine Chen, Scott Carter, Tatiana Lau, Nayeli Suseth Bravo, Sumanta Bhattacharyya, Kate Sieck, Charlene C. Wu
| Challenge: | Empathy from perspectives is a prediction tool that uses a rater’s perspective to predict the rater's empathy towards a story. |
| Approach: | They developed a model that uses a rater’s perspective as context for predicting the rater's empathy towards a story. |
| Outcome: | The proposed model improves on the EmpathyFromPerspectives dataset and compares it with baseline models. |
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
| Approach: | They propose a disjunctive model that computes the contribution of content and context separately. |
| Outcome: | The proposed model outperforms baseline models which use popularity as informativeness measure. |
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking (N18-1)
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| Challenge: | Existing methods to disentangle interleaved conversations can lead to difficulties in following discussions and retrieving relevant information from simultaneous messages. |
| Approach: | They propose to leverage representation learning to separate intermingled messages into detached conversations by estimating conversation-level similarity between closely posted messages. |
| Outcome: | The proposed approach outperforms baselines in pairwise similarity estimation and conversation disentanglement. |
Adversarial Domain Adaptation Using Artificial Titles for Abstractive Title Generation (P19-1)
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| Challenge: | Obtaining good quality labeled data can be difficult and expensive for abstractive summarization models . authors propose the use of artificial titles for unlabeled target documents . |
| Approach: | They propose to use artificial titles and sequential training to capture grammatical style of unlabeled target domains to adapt to/from news articles and Stack Exchange posts. |
| Outcome: | The proposed techniques can boost performance for unsupervised adaptation and fine-tuning with limited target data. |