3 accepted papers
Concept-based models can map black-box representations to human-understandable concepts, which makes the decision-making process more transparent and then allows users to understand the reason behind predictions. However, domain-specific concepts often impact the final predictions, which subsequentl…
Embedding-based neural topic models have turned out to be a superior option for low-resourced topic modeling. However, current approaches consider static word embeddings learnt from source tasks as general knowledge that can be transferred directly to the target task, discounting the dynamically cha…
Existing deep topic models are effective in capturing the latent semantic structures in textual data but usually rely on a plethora of documents. This is less than satisfactory in practical applications when only a limited amount of data is available. In this paper, we propose a novel framework that…