Bitext


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What do you evaluate in your chatbots? Some ideas

[fa icon="calendar'] Nov 22, 2019 6:12:32 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, POS tagging, AI, Multilanguage, NLP for Core, NLP for Chatbots, "Multilingual synthetic data"

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You have a chatbot up and running, offering help to your customers. But how do you know whether the help you are providing is correct or not? Evaluating chatbots can be complex, especially because it is affected by many factors. 

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Manual Training: Why it won't work for you

[fa icon="calendar'] Nov 19, 2019 5:25:06 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, POS tagging, AI, Multilanguage, NLP for Core, NLP for Chatbots, "Multilingual synthetic data"

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All machine learning engines (including the ones that make chatbots work) need training data to be useful. The better the training data is, the better results you will get. What’s a data scientist to do if they lack sufficient data to train a machine learning model? 

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How to solve data scarcity for AI

[fa icon="calendar'] Nov 6, 2019 6:30:00 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, POS tagging, AI, NLP for Core, NLP for Chatbots, "Multilingual synthetic data"

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Data scarcity is one of the major bottlenecks for Artificial Intelligence (AI) to reach production levels. The reason is simple: data, or the lack of it, is the number one reason why AI/Natural Language Understanding (NLU) projects fail. So the AI community is working extremely hard to come up with a solution.

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Bitext, an Award-Winning Artificial Intelligence Company for NLP and Multilingual Synthetic Training Data

[fa icon="calendar'] Oct 22, 2019 6:15:00 PM / by Bitext posted in API, Machine Learning, NLP, Semantic Analysis, Sentiment Analysis, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Text Categorization, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLU, POS tagging, AI, Entity extraction, NLP for Core

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Bitext’s is industrializing training data production for any voice-controlled device, chatbot or IVR using artificial training data to accelerate customer support automation. At Bitext we solve data scarcity and legal risks with Multilingual Synthetic Training Data to enhance Conversational AI and to derive insights from text-based and unstructured data such as contact center interactions, chat-bot and live chat transcripts, product reviews, open-ended survey responses and email. We can natively analyze text in up to 80 languages.

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Training AI Faster with a Human in the Loop

[fa icon="calendar'] Oct 16, 2019 5:30:00 PM / by Bitext posted in Machine Learning, Bitext, Natural Language, Artificial Intelligence, Chatbots, NLG, NLU, AI, NLP for Chatbots, "Multilingual synthetic data"

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One of the most significant aspects of a virtual agent is how fast it can learn. With a human in the conversational loop, training AI goes much faster: your bot learns and changes, keeping knowledge up to date. Plus, users never get the “Sorry, I did not understand your request” response, your brand will be able to solve the problem right away.

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AI and chatbots: How to design a great conversation?

[fa icon="calendar'] Sep 16, 2019 4:56:38 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Natural Language, Artificial Intelligence, Deep Learning, Chatbots, Phrase Extraction, NLG, TechCrunch, NLU, AI, Multilanguage, NLP for Core, NLP for Chatbots

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Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when user deviates from the designed linear flow.

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