Papers by Chunping Liu
Causal Intervention-based Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
| Approach: | They propose a causal intervention-based few-shot named entity recognition method that blocks the backdoor path between context and label. |
| Outcome: | The proposed method achieves state-of-the-art in a few-shot named entity recognition (NER) task. |
Hierarchy-Aware Global Model for Hierarchical Text Classification (2020.acl-main)
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| Challenge: | Existing methods for hierarchical text classification are limited and lack holistic structural information. |
| Approach: | They propose a hierarchy-aware global model with two variants that learn hierarchy-based label embeddings through an encoder and conduct inductive fusion of label-alike text features. |
| Outcome: | The proposed model improves on three benchmark datasets. |
TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling (2024.lrec-main)
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| Challenge: | Existing methods for visual storytelling ignore latent topic information. |
| Approach: | They propose a topic-aware reinforcement network for VIsual StoryTelling that takes topic information into account to generate a coherent story. |
| Outcome: | The proposed method outperforms most of the competing models across multiple evaluation metrics. |
CoVariance-based Causal Debiasing for Entity and Relation Extraction (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition and Relation Extraction are key tasks of Information Extraction. |
| Approach: | They propose a causal framework called c ovariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. |
| Outcome: | The proposed framework minimizes characterizing features’ covariance for alleviating selection and distribution bias and enhances feature representation in the feature space. |
RanLoRA: Residual-aware Nonlinear Low-Rank Adaptation (2026.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) relying on linear low-rank projections restricts adaptation to linear subspaces, limiting flexibility on complex downstream tasks. |
| Approach: | They propose a nonlinear low-rank Adaptation approach that leverages pretrained weights to decompose them into principal components that are kept frozen and residual components that can be used for task-specific adaptation. |
| Outcome: | The proposed approach outperforms vanilla LoRA and representative variants on commonsense reasoning, image classification, and mathematical reasoning tasks. |
Visual-Textual Alignment for Graph Inference in Visual Dialog (2020.coling-main)
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| Challenge: | Existing approaches to visual dialog do not understand semantic dependencies between visual and textual contents. |
| Approach: | They propose a Visual-Textual Alignment for Graph Inference network that makes up the lack of structural inference in visual dialog. |
| Outcome: | The proposed model outperforms existing models on a VisDial dataset. |