Papers by Honglei Guo
Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement (2021.emnlp-main)
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| Challenge: | Existing methods to generate mind-maps from text are difficult to capture the overall semantics of a document. |
| Approach: | They propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. |
| Outcome: | The proposed network reduces inference time by thousands of times compared with existing methods and reveals key semantic structures better than plain text. |
Multi-Label Few-Shot Learning for Aspect Category Detection (2021.acl-long)
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| Challenge: | Existing few-shot learning methods focus on single-label predictions, which can not work well for ACD since a sentence may contain multiple aspect categories. |
| Approach: | They propose a few-shot learning method that uses the prototypical network to learn aspects from a set of aspects. |
| Outcome: | The proposed method significantly outperforms baseline methods on three datasets. |
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification (D19-1)
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| Challenge: | Existing approaches to cross-domain sentiment classification focus on domain-invariant representations, but few focus on the domain-specific information. |
| Approach: | They propose to distill domain-invariant sentiment features with an orthogonal domain-dependent task . the orthogonalist task is built on the aspects varying widely in different domains . |
| Outcome: | The proposed method improves domain-invariant features and transfer performance on three public datasets. |
Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions (2025.findings-emnlp)
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| Challenge: | Synthetic therapy dialogues generated by large language models (LLMs) lack the nuanced emotional dynamics of real therapy. |
| Approach: | They introduce a dataset of authentic cognitive behavioral therapy dialogues and analyze emotional arcs between real and LLM-generated CBT sessions. |
| Outcome: | The proposed dataset is a comparative analysis of emotional arcs between real and LLM-generated CBT sessions. |