Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious deficiencies. (1) They usually consider only a single granularity of modification strategies (e.g. word-level or sentence-level), which is insufficient to explore the holistic textual space for generation; (2) They need to query victim models hundreds of times to make a successful attack, which is highly inefficient in practice. To address such problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models. Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times. Additionally, we also adapt the agent to attack blackbox models that only output labels without confidence scores. We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets. Experimental results show that our models achieve overall better attacking performance and produce more fluent and grammatical adversarial samples compared to baseline models. Besides, our adversarial attack agent significantly reduces the query times in both attack settings. Our codes are released at https://github. com/Yangyi-Chen/MAYA.
Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes classification focus on the improvement of the classification algorithm, ignoring the selection of training data which has a great effect on the performance of classifier. And so a method is proposed to optimize the selection of training data in this paper. Adopting this method, the noisy instances in training data are eliminated by user-defined effectiveness threshold, improving the performance of classifier. Experimental results on large-scale data show that our approach significantly outperforms the baseline classifier.
Human-computer interaction system is the medium for human and computer. The rationality and intelligence of its design directly affect the work efficiency and execution ability of relevant practitioners. Traditional human-computer interaction evaluation usually adopts expert evaluation method. This method is difficult to evaluate objectively because of people's subjective cognitive differences. Therefore, this paper proposes an intelligent evaluation method for complex human-computer interaction system based on BP neural network model. First, the known evaluation indicators are classified and organized, and five key evaluation indicators are optimized according to importance and relevance. Then the index is quantified into the evaluation function according to the fuzzy analytic hierarchy process. Finally, the data obtained by the simulation test is used as the training set and test set of the BP neural network, and then the evaluation model of the humancomputer interaction system is obtained.
Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have obvious deficiencies. (1) They usually consider only a single granularity of modification strategies (e.g. word-level or sentence-level), which is insufficient to explore the holistic textual space for generation; (2) They need to query victim models hundreds of times to make a successful attack, which is highly inefficient in practice. To address such problems, in this paper we propose MAYA, a Multi-grAnularitY Attack model to effectively generate high-quality adversarial samples with fewer queries to victim models. Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times. Additionally, we also adapt the agent to attack blackbox models that only output labels without confidence scores. We conduct comprehensive experiments to evaluate our attack models by attacking BiLSTM, BERT and RoBERTa in two different black-box attack settings and three benchmark datasets. Experimental results show that our models achieve overall better attacking performance and produce more fluent and grammatical adversarial samples compared to baseline models. Besides, our adversarial attack agent significantly reduces the query times in both attack settings. Our codes are released at https://github. com/Yangyi-Chen/MAYA.
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