Papers by Lifang He
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks (2020.coling-main)
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| Challenge: | Recent work on data augmentation techniques that interpolate inputs and labels shows strong effectiveness in image classification. |
| Approach: | They propose to integrate mixup to transformer-based pre-trained architecture for NLP tasks while keeping the whole end-to-end training system. |
| Outcome: | The proposed framework improves on GLUEbenchmark and transformer-based learning models while keeping the whole end-to-end training system. |
Scattered Hypothesis Generation for Open-Ended Event Forecasting (2026.findings-acl)
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| Challenge: | Existing methods for event forecasting focus on the most probable outcomes, neglecting the intrinsic uncertainty of real-world events. |
| Approach: | They propose a reinforcement learning framework that optimizes inclusiveness and diversity of the hypothesis by integrating validity-gated score into the overall objective. |
| Outcome: | The proposed framework outperforms baselines on two real-world benchmark datasets. |
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for summarizing semantic graph structure from raw text are cumbersome and inefficient for long-text documents. |
| Approach: | They propose a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. |
| Outcome: | The proposed model performs state-of-the-art on single- and multi-document summarization tasks while using less memory and fewer parameters. |
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)
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| Challenge: | Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process . |
| Approach: | They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders . |
| Outcome: | The proposed approach can be used to make an effective decision-making tool for the academic paper review process. |