Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions
- 주제(키워드) Robustness , Natural language processing , Deep learning , Measurement , Predictive models , Data models , Benchmark testing , Speech recognition , Sentiment analysis , Production systems , Natural language processing , adversarial attacks , robustness
- 주제(기타) Computer Science, Information Systems
- 주제(기타) Engineering, Electrical & Electronic
- 주제(기타) Telecommunications
- 설명문(일반) [Omar, Marwan; Choi, Soohyeon; Mohaisen, David] Univ Cent Florida, Dept Comp Sci, Orlando, FL 32816 USA; [Omar, Marwan] IIT, ITM Dept, Chicago, IL 60616 USA; [Nyang, Daehun] Ewha Womans Univ, Cyber Secur Dept, Seoul, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 Green Submitted, gold
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2022
- 세부유형 Article
- URI http://www.dcollection.net/handler/ewha/000000194536
- 본문언어 영어
- Published As https://doi.org/10.1109/ACCESS.2022.3197769
초록/요약
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
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