Papers with head-to-head comparisons of
Improving Neural Topic Models using Knowledge Distillation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current paradigms for transfer learning use general knowledge as a foundation for more specialized endeavors. |
| Approach: | They propose to combine probabilistic topic models and pretrained transformers to improve topic quality by using knowledge distillation. |
| Outcome: | The proposed framework improves topic quality over all estimated topics and in head-to-head comparisons of aligned topics. |