Abstract
Meta-embedding learning, which combines complementary information in different word embeddings, have shown superior performances across different Natural Language Processing tasks. However, domain-specific knowledge is still ignored by existing meta-embedding methods, which results in unstable performances across specific domains. Moreover, the importance of general and domain word embeddings is related to downstream tasks, how to regularize meta-embedding to adapt downstream tasks is an unsolved problem. In this paper, we propose a method to incorporate both domain-specific and task-oriented information into meta-embeddings. We conducted extensive experiments on four text classification datasets and the results show the effectiveness of our proposed method.- Anthology ID:
- 2020.emnlp-main.282
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3508–3513
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.282
- DOI:
- 10.18653/v1/2020.emnlp-main.282
- Cite (ACL):
- Xin Wu, Yi Cai, Yang Kai, Tao Wang, and Qing Li. 2020. Task-oriented Domain-specific Meta-Embedding for Text Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3508–3513, Online. Association for Computational Linguistics.
- Cite (Informal):
- Task-oriented Domain-specific Meta-Embedding for Text Classification (Wu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.282.pdf