Papers by Siyu Long
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
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| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (2021.emnlp-main)
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| Challenge: | Large-scale multi-label text classification tasks often face long-tailed label distributions, where many labels have few or even no training instances. |
| Approach: | They propose a meta-learning approach that incorporates the objective of adapting to new low-resource tasks into the meta-Learning phase. |
| Outcome: | The proposed approach achieves state-of-the-art against strong baselines and can still enhance powerful BERTlike models. |
MUSE: MCTS-Driven Red Teaming Framework for Enhanced Multi-Turn Dialogue Safety in Large Language Models (2025.emnlp-main)
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Siyu Yan, Long Zeng, Xuecheng Wu, Chengcheng Han, Kongcheng Zhang, Chong Peng, Xuezhi Cao, Xunliang Cai, Chenjuan Guo
| Challenge: | Existing defenses target single-turn attacks, but real-world usage involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures. |
| Approach: | They propose a framework that tackles multi-turn jailbreaks from both attack and defense angles. |
| Outcome: | Experiments on large language models show that MUSE effectively mitigates multi-turn jailbreaks. |
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension (2020.coling-main)
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| Challenge: | Experimental results show that our model achieves state-of-the-art performance for Chinese idiom comprehension. |
| Approach: | They propose a model that can mitigate the inconsistency between literal and literal meanings by incorporating the synonym knowledge enhanced reader into the model. |
| Outcome: | The proposed model achieves state-of-the-art on a Chinese idiom reading comprehension dataset. |