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  • Association for Computing Machinery (ACM)  (2)
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  • Association for Computing Machinery (ACM)  (2)
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  • 1
    Online Resource
    Online Resource
    Domain-Invariant Feature Progressive Distillation with Adversarial Adaptive Augmentation for Low-Resource Cross-Domain NER
    Association for Computing Machinery (ACM) ; 2023
    In:  ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 22, No. 3 ( 2023-03-31), p. 1-21
    Details
    In: ACM Transactions on Asian and Low-Resource Language Information Processing, Association for Computing Machinery (ACM), Vol. 22, No. 3 ( 2023-03-31), p. 1-21
    Abstract: Considering the expensive annotation in Named Entity Recognition (NER ), Cross-domain NER enables NER in low-resource target domains with few or without labeled data, by transferring the knowledge of high-resource domains. However, the discrepancy between different domains causes the domain shift problem and hampers the performance of cross-domain NER in low-resource scenarios. In this article, we first propose an adversarial adaptive augmentation, where we integrate the adversarial strategy into a multi-task learner to augment and qualify domain adaptive data. We extract domain-invariant features of the adaptive data to bridge the cross-domain gap and alleviate the label-sparsity problem simultaneously. Therefore, another important component in this article is the progressive domain-invariant feature distillation framework. A multi-grained MMD (Maximum Mean Discrepancy) approach in the framework to extract the multi-level domain invariant features and enable knowledge transfer across domains through the adversarial adaptive data. Advanced Knowledge Distillation (KD) schema processes progressively domain adaptation through the powerful pre-trained language models and multi-level domain invariant features. Extensive comparative experiments over four English and two Chinese benchmarks show the importance of adversarial augmentation and effective adaptation from high-resource domains to low-resource target domains. Comparison with two vanilla and four latest baselines indicates the state-of-the-art performance and superiority confronted with both zero-resource and minimal-resource scenarios.
    Type of Medium: Online Resource
    ISSN: 2375-4699 , 2375-4702
    URL: Article
    DOI: 10.1145/3570502
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2023
    detail.hit.zdb_id: 2820615-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    A Survey on Text Classification: From Traditional to Deep Learning
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Intelligent Systems and Technology Vol. 13, No. 2 ( 2022-04-30), p. 1-41
    Details
    In: ACM Transactions on Intelligent Systems and Technology, Association for Computing Machinery (ACM), Vol. 13, No. 2 ( 2022-04-30), p. 1-41
    Abstract: Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
    Type of Medium: Online Resource
    ISSN: 2157-6904 , 2157-6912
    URL: Article
    DOI: 10.1145/3495162
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2584437-4
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