A survey on neural topic models: methods, applications, and challenges
Topic models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …
proportions of documents in an unsupervised fashion. They have been widely used in …
Effective neural topic modeling with embedding clustering regularization
Topic models have been prevalent for decades with various applications. However, existing
topic models commonly suffer from the notorious topic collapsing: discovered topics …
topic models commonly suffer from the notorious topic collapsing: discovered topics …
Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
Data quality stands at the forefront of deciding the effectiveness of video-language
representation learning. However, video-text pairs in previous data typically do not align …
representation learning. However, video-text pairs in previous data typically do not align …
AKEW: Assessing knowledge editing in the wild
Abstract Knowledge editing injects knowledge updates into language models to keep them
correct and up-to-date. However, its current evaluations deviate significantly from practice …
correct and up-to-date. However, its current evaluations deviate significantly from practice …
On the affinity, rationality, and diversity of hierarchical topic modeling
Hierarchical topic modeling aims to discover latent topics from a corpus and organize them
into a hierarchy to understand documents with desirable semantic granularity. However …
into a hierarchy to understand documents with desirable semantic granularity. However …
Are LLMs good zero-shot fallacy classifiers?
Fallacies are defective arguments with faulty reasoning. Detecting and classifying them is a
crucial NLP task to prevent misinformation, manipulative claims, and biased decisions …
crucial NLP task to prevent misinformation, manipulative claims, and biased decisions …
Infoctm: A mutual information maximization perspective of cross-lingual topic modeling
Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing
aligned latent topics. However, most existing methods suffer from producing repetitive topics …
aligned latent topics. However, most existing methods suffer from producing repetitive topics …
Improving multimodal sentiment analysis: Supervised angular margin-based contrastive learning for enhanced fusion representation
The effectiveness of a model is heavily reliant on the quality of the fusion representation of
multiple modalities in multimodal sentiment analysis. Moreover, each modality is extracted …
multiple modalities in multimodal sentiment analysis. Moreover, each modality is extracted …
Contrastive Multi-View Interest Learning for Cross-Domain Sequential Recommendation
Cross-domain recommendation (CDR), which leverages information collected from other
domains, has been empirically demonstrated to effectively alleviate data sparsity and cold …
domains, has been empirically demonstrated to effectively alleviate data sparsity and cold …
A multi-modal contrastive diffusion model for therapeutic peptide generation
Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the
treatment of human diseases. Recently, deep generative models have exhibited remarkable …
treatment of human diseases. Recently, deep generative models have exhibited remarkable …