What do you evaluate in your chatbots? Some ideas

[fa icon="calendar'] May 31, 2021 10:00:00 AM / 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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In this blog we will discuss three ways of doing your chatbot evaluation by using:

  1. real world evaluation data
  2. synthetic data
  3. "in scope" or "out of scope" queries
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?  Chatbot evaluation can be complex, especially because it is affected by many factors. 
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How chatbots enhance customer experience in contact centers

[fa icon="calendar'] May 25, 2021 5:00:00 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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Chatbots can improve customer experience in contact centers by:

  • Reducing customer wait time
  • Achieving a higher customer satisfaction
  • Cutting down contact center expenses and increasing productivity
  • Getting to know your customer better
  • Using human agents only when it is necessary

Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.

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AI and chatbots: How to design a great conversation?

[fa icon="calendar'] May 11, 2021 5:52:38 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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The following practices will help you design a great conversation between your chatbot and your client:

  • Transparency
  • Avoid using an excess of predetermined links and buttons
  • Use an NLP middleware approach
  • Include politeness and small talk
  • Tolerate typos and slightly ambiguous formulations
  • Always include domain specific terminology
  • Define your bot's tone
  • Keep the number of possibilities limited
  • Let the user know when the bot is "thinking" or processing the query

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 the user deviates from the designed linear flow.

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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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Noisy text is realistic text

[fa icon="calendar'] Feb 24, 2020 4:45:00 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, NLG, NLU, Query Rewriting, AI, Multilanguage, NLP for Core, NLP for Chatbots, NLP for CX, "Multilingual synthetic data"

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One of the flaws of usual training data generation is that, when you ask somebody to manually create training data for you, they will make an effort to write these sentences correctly, following the spelling and punctuation norms of your language. Even if some errors appear, they will be minimal, because they are trying to do things right —this is, to provide “orthographically right” sentences.

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