Papers by Sajad Mirzababaei
Hengam: An Adversarially Trained Transformer for Persian Temporal Tagging (2022.aacl-main)
Copied to clipboard
| Challenge: | A wide array of natural language processing (NLP) applications relies on accurately identifying events and their respective occurrence times. |
| Approach: | They propose an adversarially trained transformer for Persian temporal tagging that can generalize over the HengamTagger’s rules. |
| Outcome: | The proposed tool outperforms state-of-the-art methods on a diverse and manually created dataset. |