Papers by Shuo Liang
CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection (2024.findings-acl)
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| Challenge: | Existing methods for social media bot detection neglect community structure and poor model generalization due to the relatively small scale of the dataset. |
| Approach: | They propose a framework that constructs social networks as heterogeneous graphs and uses community-aware modules to mine hard positive and hard negative samples for supervised graph contrastive learning. |
| Outcome: | The proposed framework outperforms baselines on three social media bot benchmarks. |
What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time (2026.acl-long)
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| Challenge: | Existing TTRL methods rely on positive pseudo-labeling strategies to enhance reasoning capabilities. |
| Approach: | They propose a test-time reinforcement learning framework that mitigates label noise amplification by deriving pseudo-rewards from majority voting consensus. |
| Outcome: | The proposed framework mitigates label noise amplification by implementing selective positive pseudo-labeling and entropy-gated negative p-labeled pruning. |
Making Long-Context Language Models Better Multi-Hop Reasoners (2024.acl-long)
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| Challenge: | Recent advances in long-context modeling have enhanced language models for complex tasks, but they struggle with multi-hop reasoning and noisy contexts. |
| Approach: | They propose an approach that prompts LMs to supply attributions for each assertion during reasoning. |
| Outcome: | The proposed model achieves competitive performance on multi-hop reasoning benchmarks, closely paralleling proprietary LMs such as ChatGPT and Claude-instant. |
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Aspect-based sentiment analysis is challenging because a sentence may contain multiple aspects or complicated relationships. |
| Approach: | They propose a bi-syntax aware Graph Attention Network to model the context of every aspect and sentiment relations across aspects for learning. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on four benchmark datasets. |