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Register a new userInFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding
Infobert: نهج الطلقة الصفرية لفهم اللغة الطبيعية باستخدام كلمة التضمين
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فهم اللغة الطبيعية مهمة مهمة في أنظمة الحوار الحديثة.يصبح أكثر أهمية مع التمديد السريع لوظيفة أنظمة الحوار.في هذا العمل، نقدم نهجا لتعلم تحويل الصفر بالرصاص لمهام تصنيف النوايا وملء الفتحات بناء على نماذج اللغة المدربة مسبقا.نستخدم نماذج محكسية عميقة تغذيها مع الكلام وأوصاف اللغة الطبيعية لحالة المستخدم للحصول على embeddings.ثم تستخدم هذه المدينات من قبل شبكة عصبية صغيرة لإنتاج تنبؤات للحصول على الاحتمالات النية والفتحة.تحقق هذه الهندسة المعمارية نتائج جديدة من الفنون الجديدة في سيناريوهات صفرية بالرصاص.واحدة هي لغة واحدة تتكيف مع المهارات الجديدة وآخر هو التكيف عبر اللغات.
Natural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems' functionality. In this work, we present an approach to zero-shot transfer learning for the tasks of intent classification and slot-filling based on pre-trained language models. We use deep contextualized models feeding them with utterances and natural language descriptions of user intents to get text embeddings. These embeddings then used by a small neural network to produce predictions for intent and slot probabilities. This architecture achieves new state-of-the-art results in two zero-shot scenarios. One is a single language new skill adaptation and another one is a cross-lingual adaptation.
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In this paper, we introduce our system that we participated with at the multilingual and cross-lingual word-in-context disambiguation SemEval 2021 shared task. In our experiments, we investigated the possibility of using an all-words fine-grained wor
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Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications. However, one neglected area of research is the impact of noisy (corrupted) labels on KD. We present,
to the best of our knowledge, the first study on KD with noisy labels in Natural Language Understanding (NLU). We document the scope of the problem and present two methods to mitigate the impact of label noise. Experiments on the GLUE benchmark show that our methods are effective even under high noise levels. Nevertheless, our results indicate that more research is necessary to cope with label noise under the KD.
Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, whi
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In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels.
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