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Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions
- Title
- Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions
- Authors
- Omar, Marwan; Choi, Soohyeon; Nyang, Daehun; Mohaisen, David
- Ewha Authors
- 양대헌
- SCOPUS Author ID
- 양대헌
- Issue Date
- 2022
- Journal Title
- IEEE ACCESS
- ISSN
- 2169-3536
- Citation
- IEEE ACCESS vol. 10, pp. 86038 - 86056
- Keywords
- Robustness; Natural language processing; Deep learning; Measurement; Predictive models; Data models; Benchmark testing; Speech recognition; Sentiment analysis; Production systems; adversarial attacks; robustness
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Recent natural language processing (NLP) techniques have accomplished high performance on benchmark data sets, primarily due to the significant improvement in the performance of deep learning. The advances in the research community have led to great enhancements in state-of-the-art production systems for NLP tasks, such as virtual assistants, speech recognition, and sentiment analysis. However, such NLP systems still often fail when tested with adversarial attacks. The initial lack of robustness exposed troubling gaps in current models' language understanding capabilities, creating problems when NLP systems are deployed in real life. In this paper, we present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deep-dive into the various dimensions of robustness, across techniques, metrics, embedding, and benchmarks. Finally, we argue that robustness should be multi-dimensional, provide insights into current research, identify gaps in the literature to suggest directions worth pursuing to address these gaps
- DOI
- 10.1109/ACCESS.2022.3197769
- Appears in Collections:
- 인공지능대학 > 사이버보안학과 > Journal papers
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