Papers by Jianfei Jiang
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network (D18-1)
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| Challenge: | Existing approaches to emotion detection are lexicon-based, graphical model-based and linear classifier-based. |
| Approach: | They propose a transfer learning architecture to divide sentence representation into two different feature spaces which capture general sentiment words and other important emotion-specific words via a dual attention mechanism. |
| Outcome: | The proposed model can capture general sentiment words and other emotion-specific words via a dual attention mechanism on two benchmark datasets. |
Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations (2020.emnlp-main)
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| Challenge: | Existing approaches to rumor verification and stance classification fail to exploit intertask dependencies . |
| Approach: | They propose a Hierarchical Transformer model which uses BERT to obtain thread representations . they propose 'coupled' transformer modules to capture intertask interactions and a post-level attention layer to use predicted stance labels for RV. |
| Outcome: | The proposed model outperforms existing methods on two benchmark datasets. |
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020.acl-main)
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| Challenge: | Existing methods for named entity recognition ignore visual context bias . NER is a key component of many information extraction tasks . |
| Approach: | They propose to use a multimodal interaction module to generate word-aware visual representations and leverage purely text-based entity span detection as an auxiliary module to guide the final predictions. |
| Outcome: | The proposed approach achieves state-of-the-art on two benchmark datasets. |
Bridging the Temporal Gap in Multimodal LLMs: Deeply Stacking Temporal Tokens for Audio-Visual Speech Recognition (2026.findings-acl)
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| Challenge: | Existing audio-visual speech recognition systems suffer from a temporal gap . visual speech patterns captured from lip movements provide complementary information that remains inherently robust to acoustic noise. |
| Approach: | They propose a framework that deeply stacks temporal tokens across both encoding and decoding stages to bridge this temporal gap. |
| Outcome: | The proposed framework outperforms existing supervised, self-supervised, and LLM-based methods by 6.1% on LRS2 and 7.8% on LLS3. |