Siri Speaking Arabic: What Is Failing?

[fa icon="calendar'] Apr 1, 2021 2:04:30 AM / by Bitext posted in NLP for Core

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Almost three years after Apple launched its well-known voice assistant Siri for the Arabic language, there is still room for further improvement. Siri can currently understand more than 20 languages and dialects; but, when it comes to Arabic, its abilities are not good enough to fully understand what users need. Several utterance errors together with poor understanding skills are quite frustrating for Arabic speakers. What’s going wrong here?

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Natural Language Understanding (NLU) for Audio Requires a Highly Accurate and Fast Speech to Text Foundation

[fa icon="calendar'] Mar 31, 2021 6:44:06 PM / by Bitext and Deepgram posted in NLP, Artificial Intelligence, Chatbots, NLU, AI, Multilanguage, NLP for Chatbots, IVR, voicebots, improvement of NLU models, ASR, Deepogram

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As companies consider adding voicebots to their customer service, they are experiencing the same "Achilles’ heel" with NLU voicebots as they do with chatbots.These issues are the following (the fourth one only applies to voicebots):

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NLU Chatbot Evaluation- 3 Common Errors and 5 Key Steps

[fa icon="calendar'] Feb 18, 2021 10:40:13 PM / by Bitext posted in Natural Language, Artificial Intelligence, Chatbots, NLU, RASA, AI, NLP for Chatbots, aws

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Why is Bitext's Text Generation Unique?

[fa icon="calendar'] Nov 16, 2020 7:15:12 PM / by Bitext posted in Machine Learning, NLP, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, NLG, Multilanguage

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NLU vs. ITR Chatbots... Which one should I use?

[fa icon="calendar'] Oct 15, 2020 5:03:33 PM / by Bitext posted in Machine Learning, NLP, Natural Language, Artificial Intelligence, Chatbots, NLU, AI, NLP for Chatbots

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Larger Language Models are Key to Higher Accuracy? So Says Nvidia

[fa icon="calendar'] Jun 8, 2020 6:13:37 PM / by Bitext

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The Nvidia team/blog cannot be clearer about the challenges of AI & NLP: “Recent work has demonstrated that larger language models dramatically advance the state of the art in natural language processing (NLP) applications such as question-answering, dialog systems, summarization, and article completion. However, during training, large models do not fit in the available memory of a single accelerator, requiring model parallelism to split the parameters across multiple accelerators.” See details at "State-of-the-Art Language Modeling Using Megatron on the NVIDIA A100 GPU" https://devblogs.nvidia.com/language-modeling-using-megatron-a100-gpu

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