Papers by Deqiang Jiang
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net (2022.findings-naacl)
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| Challenge: | Existing methods focus on attention mechanism, but they are not suitable for abstractive text summarization. |
| Approach: | They propose a siamese generative adversarial net for abstractive text summarization which preserves the main semantics of the source text and the target summary. |
| Outcome: | The proposed model can preserve the main semantics of the source text and target summary. |
Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation (2024.lrec-main)
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| Challenge: | Existing acceleration methods for text generation ignore the importance of the distribution of sampling steps, resulting in slow sampling rates. |
| Approach: | They propose a technique to accelerate diffusion models for text generation without additional training by using a Bayesian optimization approach. |
| Outcome: | The proposed technique achieves 400x acceleration even with minimal sampling steps after down to less than 1 minute of optimization yielding a competitive performance even with minimum sampling steps. |
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations (2021.emnlp-main)
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| Challenge: | Existing approaches to hierarchical multi-label text classification ignore vertical category correlations or exploit dependencies across levels without considering horizontal correlations . |
| Approach: | They propose a hierarchical multi-label text classification framework that considers both vertical and horizontal category correlations. |
| Outcome: | The proposed framework improves on real-world HMTC datasets with significant improvements over baselines. |
GMN: Generative Multi-modal Network for Practical Document Information Extraction (2022.naacl-main)
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| Challenge: | Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world. |
| Approach: | They propose a multi-modal generation method without predefined label categories for real-world scenarios using a spatial encoder and modal-aware mask module. |
| Outcome: | The proposed method can deal with complex documents that are hard to serialize into sequential order. |