Papers by Chunping Li
Discovering Topics in Long-tailed Corpora with Causal Intervention (2021.findings-acl)
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| Challenge: | Existing topic models are designed and evaluated on balanced corpora, but the longtailed bias can impair the performance of topic models. |
| Approach: | They propose a causal inference framework to explain topic modeling on long-tailed corpora by applying causal intervention in training. |
| Outcome: | The proposed model can mitigate the bias effect, greatly improve topic quality and discover the hidden semantics on the tail. |
Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 Loss (2024.emnlp-main)
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| Challenge: | despite its efficiency, Sentence-BERT ignores the progressive nature of semantic relationships, despite a promising approach . contrastive learning methods have improved performance on renowned STS benchmarks, but they fail to leverage fine-grained information. |
| Approach: | They propose a regression framework that categorizes text pairs as either semantically similar or dissimilar . they propose two loss functions: Translated ReLU and Smooth K2 Loss to bridge this gap . |
| Outcome: | The proposed method achieves convincing performance across seven established STS benchmarks. |
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. |
Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual Similarity (2024.emnlp-main)
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| Challenge: | Semantic Textual Similarity (STS) is a key indicator of the encoding capabilities of embedding models. |
| Approach: | They propose to use Pearson’s correlation coefficient as a loss function to refine model performance beyond contrastive learning to achieve a Spearman’s ceiling. |
| Outcome: | The proposed method surpasses state-of-the-art strategies with minimal amount of fine-grained annotated samples. |
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder (2020.emnlp-main)
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| Challenge: | Topic models for short texts suffer from data sparsity because of limited word co-occurrences. |
| Approach: | They propose a neural topic model with a new topic distribution quantization approach that generates peakier distributions that are more appropriate for modeling short texts. |
| Outcome: | The proposed model outperforms both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics. |