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Register a new userCertified Robustness to Programmable Transformations in LSTMs
متانة معتمدة للتحولات القابلة للبرمجة في LSTMS
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الشبكات العصبية العميقة لمعالجة اللغات الطبيعية هشة في مواجهة أمثلة الخصومة --- اضطرابات صغيرة في الإدخال، مثل استبدال مرادف أو تكرار Word، والذي يسبب شبكة عصبية لتغيير تنبؤها.نقدم نهجا لإنشاء متانة LSTMS (وملحقات LSTMS) ونماذج التدريب التي يمكن اعتمادها بكفاءة.يمكن أن تؤدي نهجنا إلى التصديق على المتانة على أماكن الاضطرابات الكبيرة غير المحددة برمجيا بلغة تحويلات السلسلة.يوضح تقييمنا أن نهجنا يمكن أن تدريب النماذج الأكثر قوة لمجموعات من تحويلات السلسلة من تلك التي تم إنتاجها باستخدام التقنيات الحالية؛(2) نهجنا يمكن أن تظهر دقة شهادة عالية من النماذج الناتجة.
Deep neural networks for natural language processing are fragile in the face of adversarial examples---small input perturbations, like synonym substitution or word duplication, which cause a neural network to change its prediction. We present an approach to certifying the robustness of LSTMs (and extensions of LSTMs) and training models that can be efficiently certified. Our approach can certify robustness to intractably large perturbation spaces defined programmatically in a language of string transformations. Our evaluation shows that (1) our approach can train models that are more robust to combinations of string transformations than those produced using existing techniques; (2) our approach can show high certification accuracy of the resulting models.
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The robustness and security of natural language processing (NLP) models are significantly important in real-world applications. In the context of text classification tasks, adversarial examples can be designed by substituting words with synonyms unde
r certain semantic and syntactic constraints, such that a well-trained model will give a wrong prediction. Therefore, it is crucial to develop techniques to provide a rigorous and provable robustness guarantee against such attacks. In this paper, we propose WordDP to achieve certified robustness against word substitution at- tacks in text classification via differential privacy (DP). We establish the connection between DP and adversarial robustness for the first time in the text domain and propose a conceptual exponential mechanism-based algorithm to formally achieve the robustness. We further present a practical simulated exponential mechanism that has efficient inference with certified robustness. We not only provide a rigorous analytic derivation of the certified condition but also experimentally compare the utility of WordDP with existing defense algorithms. The results show that WordDP achieves higher accuracy and more than 30X efficiency improvement over the state-of-the-art certified robustness mechanism in typical text classification tasks.
Seq2seq models have demonstrated their incredible effectiveness in a large variety of applications. However, recent research has shown that inappropriate language in training samples and well-designed testing cases can induce seq2seq models to output
profanity. These outputs may potentially hurt the usability of seq2seq models and make the end-users feel offended. To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity. The proposed training framework leverages merely a short list of profanity examples to prevent seq2seq models from generating a broader spectrum of profanity. The framework is composed of a pattern-eliminating training component to suppress the impact of language patterns with profanity in the training set, and a trigger-resisting training component to provide certified robustness for seq2seq models against intentionally injected profanity-triggering expressions in test samples. In the experiments, we consider two representative NLP tasks that seq2seq can be applied to, i.e., style transfer and dialogue generation. Extensive experimental results show that the proposed training framework can successfully prevent the NLP models from generating profanity.
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different forms of control and response of sub modes of PID controller, through using programmable logical controller and programmed it to be (PID) controller, and take advantage of this system for accurate control of the three phase motor speed. In addition, the PLC program is been connected to monitoring and collecting data software, known by acronym (SCADA) for calibration of (PID) and use this software to display the result of the responses for the different types of the controller PID in the form of graphic curves. These response curves show accuracy and fast to response that make this application an integrated system that could be form the infrastructure of many practical applications like large cranes, overlapping product lines and different other applications and the use of PLC systems make them high quality and high reliability systems.
Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses
a challenge for any task aiming to understand the content of the chat logs or gather information from them. The ability to disentangle these conversations is then tantamount to the success of many downstream tasks such as summarization and question answering. Structured information accompanying the text such as user turn, user mentions, timestamps, is used as a cue by the participants themselves who need to follow the conversation and has been shown to be important for disentanglement. DAG-LSTMs, a generalization of Tree-LSTMs that can handle directed acyclic dependencies, are a natural way to incorporate such information and its non-sequential nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement task. We perform our experiments on the Ubuntu IRC dataset. We show that the novel model we propose achieves state of the art status on the task of recovering reply-to relations and it is competitive on other disentanglement metrics.
Humans often employ figurative language use in communication, including during interactions with dialog systems. Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile.
In this work, we analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language. We observe large gaps in handling of figurative language when evaluating the models on two open domain dialog datasets. When faced with dialog contexts consisting of figurative language, some models show very large drops in performance compared to contexts without figurative language. We encourage future research in dialog modeling to separately analyze and report results on figurative language in order to better test model capabilities relevant to real-world use. Finally, we propose lightweight solutions to help existing models become more robust to figurative language by simply using an external resource to translate figurative language to literal (non-figurative) forms while preserving the meaning to the best extent possible.
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