@inproceedings{wu-etal-2020-task,
title = "Task-oriented Domain-specific Meta-Embedding for Text Classification",
author = "Wu, Xin and
Cai, Yi and
Kai, Yang and
Wang, Tao and
Li, Qing",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.282",
doi = "10.18653/v1/2020.emnlp-main.282",
pages = "3508--3513",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Task-oriented Domain-specific Meta-Embedding for Text Classification
%A Wu, Xin
%A Cai, Yi
%A Kai, Yang
%A Wang, Tao
%A Li, Qing
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wu-etal-2020-task
%X 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.
%R 10.18653/v1/2020.emnlp-main.282
%U https://aclanthology.org/2020.emnlp-main.282
%U https://doi.org/10.18653/v1/2020.emnlp-main.282
%P 3508-3513
Markdown (Informal)
[Task-oriented Domain-specific Meta-Embedding for Text Classification](https://aclanthology.org/2020.emnlp-main.282) (Wu et al., EMNLP 2020)
ACL