Abstract
We present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also mitigate the issues regarding unbalanced context lengths by changing the input-output format of the model such that it can deal with varying context lengths effectively. Specifically, our proposed model, trained with the proposed data augmentation technique, participated in the sarcasm detection task of FigLang2020, have won and achieves the best performance in both Reddit and Twitter datasets.
| Original language | English |
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| Title of host publication | ACL 2020 - Figurative Language Processing, Proceedings of the 2nd Workshop |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 12-17 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781952148125 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 2nd Workshop on Figurative Language Processing 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: 2020 Jul 9 → … |
Publication series
| Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
|---|---|
| ISSN (Print) | 0736-587X |
Conference
| Conference | 2nd Workshop on Figurative Language Processing 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 |
|---|---|
| Country/Territory | United States |
| City | Virtual, Online |
| Period | 20/7/9 → … |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics