Implementation of A Neural Natural Language Understanding Component for Arabic Dialogue Systems

摘要: Abstract Natural Language Understanding (NLU) is considered a core component in implementing dialogue systems. NLU has been greatly enhanced by deep learning techniques such as word embeddings and neural network architectures, but current NLP methods for Arabic language action classification or semantic decoding mostly based on handcrafted rule-based systems that use feature engineering, without the benefit of any form distributed representation words. This paper presents an approach to text Named Entity Recognition domain home automation Arabic. To this end, we present module can further be integrated with Automatic Speech (ASR), Dialogue Manager (DM) Generator (NLG) build fully working system. The describes our process collecting annotating data, structuring intent classifier entity extractor models, finally evaluation these different benchmarks.