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Register a new userEvaluating the Robustness of Neural Language Models to Input Perturbations
تقييم متانة نماذج اللغة العصبية لإجراء الاضطرابات المدخلات
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حصلت نماذج اللغة العصبية عالية الأداء على نتائج أحدث النتائج على مجموعة واسعة من مهام معالجة اللغة الطبيعية (NLP). ومع ذلك، غالبا ما لا تعكس نتائج البيانات القياسية المشتركة الموثوقية النموذجية والمتانة عند تطبيقها على بيانات صاخبة عالمية حقيقية. في هذه الدراسة، نقوم بتصميم وتنفيذ أنواع مختلفة من أساليب الاضطرابات على مستوى الأحرف ومستوى الكلمة لمحاكاة السيناريوهات الواقعية التي قد تكون فيها نصوص الإدخال صاخبة قليلا أو مختلفة عن توزيع البيانات التي تم تدريبها على أنظمة NLP عليها. إجراء تجارب شاملة على مهام NLP المختلفة، فإننا نبحث في قدرة نماذج اللغة العالية الأداء مثل Bert و Xlnet و Roberta و ELMO في التعامل مع أنواع مختلفة من اضطرابات الإدخال. تشير النتائج إلى أن نماذج اللغة حساسة للاضطرابات الإدخال ويمكن أن تنخفض أدائها حتى عند تقديم تغييرات صغيرة. نسلط الضوء على هذه النماذج تحتاج إلى مزيد من المحسن وأن المعايير الحالية لا تعكس متانة النموذج جيدا. نقول أن التقييمات بشأن المدخلات المضطربة يجب أن تكمل المعايير المستخدمة بشكل روتيني من أجل تحقيق فهم أكثر واقعية لمتانة أنظمة NLP.
High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. In this study, we design and implement various types of character-level and word-level perturbation methods to simulate realistic scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. Conducting comprehensive experiments on different NLP tasks, we investigate the ability of high-performance language models such as BERT, XLNet, RoBERTa, and ELMo in handling different types of input perturbations. The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced. We highlight that models need to be further improved and that current benchmarks are not reflecting model robustness well. We argue that evaluations on perturbed inputs should routinely complement widely-used benchmarks in order to yield a more realistic understanding of NLP systems' robustness.
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