Name
Abstract | ||
|---|---|---|
AbstractWith the purpose of learning and utilizing explicit and dense topic embeddings, we propose three variations of novel vector-quantization-based topic models (VQ-TMs): (1) Hard VQ-TM, (2) Soft VQ-TM, and (3) Multi-View Soft VQ-TM. The model family capitalize on vector quantization techniques, embedded input documents, and viewing words as mixtures of topics. Guided by a comprehensive set of evaluation metrics, we conduct systematic quantitative and qualitative empirical studies, and demonstrate the superior performance of VQ-TMs compared to important baseline models. Through a unique case study on code generation from natural language descriptions, we further illustrate the power of VQ-TMs in downstream tasks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3450946 | ACM Transactions on Intelligent Systems and Technology |
Keywords | DocType | Volume |
Knowledge discovery, deep learning, self-supervised learning | Journal | 12 |
Issue | ISSN | Citations |
3 | 2157-6904 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amulya Gupta | 1 | 0 | 0.34 |
| Zhu Zhang | 2 | 10 | 0.77 |