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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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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Linguistic Resources in +100 Languages & Variants

[fa icon="calendar'] Feb 11, 2020 2:55:24 PM / by Bitext posted in API, Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, NLG, Stemming, NLU, AI, Multilanguage, Language Identification, Decompounding, Lemmatization, NLP for Core, Finance, Banking

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All Machine Learning (ML) engines that work with text can benefit from a solid linguistic background. If they are working in a multilingual environment, the need of a good lexicon (with forms, lemmas and attributes) is overwhelming. Even so, basic features such as Word Embeddings hugely improve when enriched with linguistic knowledge, and if this is not usually applied, is because of a lack of linguists working for ML companies.

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Some figures on chatbots and eCommerce

[fa icon="calendar'] Dec 31, 2019 2:30:00 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, AI, Multilanguage, NLP for Chatbots, "Multilingual synthetic data", aws

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When we talk about the usefulness of chatbots, we sometimes look at it as a black or white matter. Yet, the issue is not whether chatbots are useful or not, it’s rather how useful they are, and whether it is worth keeping them.

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Bitext’s Customer Support Dataset for free

[fa icon="calendar'] Dec 26, 2019 5:15:00 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, AI, Multilanguage, NLP for Chatbots, "Multilingual synthetic data", aws

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We have shown in previous posts why Synthetic Training Data is the best way to boost the accuracy of any chatbot, and the solution to the most important problems of chatbots nowadays: data scarcity, namely, the lack of accurate and useful training data for the problems chatbots want to address.

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Amazon Connect: the new step into chatbots

[fa icon="calendar'] Dec 24, 2019 5:00:00 PM / by Bitext posted in Machine Learning, NLP, Big Data, Bitext, Deep Linguistic Analysis, Natural Language, Text Analytics, Artificial Intelligence, Deep Learning, Chatbots, NLU, AI, Multilanguage, NLP for Chatbots, "Multilingual synthetic data", aws

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Do you know Amazon Connect? It’s a new platform created by Amazon —and built on AWS— that allows you to have your own Contact Center in the cloud, without having to get all the infrastructure that usual contact centers need. The philosophy is “Contact Center as a Service”, and Amazon is not the only one to implement it, but it’s actually one of the most interesting ones.

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