Existing methods for named entity recognition are critically relied on labeled data. To handle the situation that the data is fully-unlabeled, we propose an entity-aware adversarial domain adaptation network, which utilizes the labeled source data and then adapts to unlabeled target domain. We first apply adversarial training to reduce the distribution gap between different domains. Furthermore, we introduce an entity-aware attention to guide adversarial process to achieve the alignment of entity features. The experiment shows that our model outperforms the state-of-the-art approaches.
|Title of host publication||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||2|
|Publication status||Published - Feb 2021|
|Event||35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Online, Virtual, Online|
Duration: 2 Feb 2021 → 9 Feb 2021
|Name||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Conference||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Period||2/02/21 → 9/02/21|
Bibliographical noteFunding Information:
This work was supported by the Fundamental Research Funds for the Central Universities, SCUT(No.D2182480), the National Key Research and Development Program of China, the Science and Technology Programs of Guangzhou (No.201802010027, 201902010046), National Natural Science Foundation of China (62076100).
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