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Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun,Diyi Yang,Xiaoya Li,Tianwei Zhang,Yuxian Meng,Han Qiu,Guoyin Wang,Eduard Hovy,Jiwei Li +8 more
TLDR
The authors provide a comprehensive review of various interpretation methods for neural models in NLP, including influence function based methods, KNN-based methods, attention based models, saliency-based models, perturbation-based method, etc.Abstract:
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks. However, a long-standing criticism against neural network models is the lack of interpretability, which not only reduces the reliability of neural NLP systems but also limits the scope of their applications in areas where interpretability is essential (e.g., health care applications). In response, the increasing interest in interpreting neural NLP models has spurred a diverse array of interpretation methods over recent years. In this survey, we provide a comprehensive review of various interpretation methods for neural models in NLP. We first stretch out a high-level taxonomy for interpretation methods in NLP, i.e., training-based approaches, test-based approaches, and hybrid approaches. Next, we describe sub-categories in each category in detail, e.g., influence-function based methods, KNN-based methods, attention-based models, saliency-based methods, perturbation-based methods, etc. We point out deficiencies of current methods and suggest some avenues for future research.read more
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Proceedings ArticleDOI
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
TL;DR: Gan et al. as mentioned in this paper presented the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NALT. 2022.
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