Papers with CREAM
A Cross-Topic Method for Supervised Relevance Classification (D19-55)
Copied to clipboard
| Challenge: | Existing approaches to relevance classification are limited by annotated data and lack of relevance for each topic. |
| Approach: | They propose a cross-topic relevance embedding aggregation methodology that can expand the range of training data and apply what has been learned from source topics to a target topic. |
| Outcome: | The proposed method can capture common features within small amount of annotated data and improve performance compared with baselines. |