Skip to main content

The Importance of Intents and Context in Chatbots

Humans are good in conversations, because they can understand the intent of any statement, also the context in which the statement is placed.

Today’s chatbots are equipped with intent recognition engine. Using machine learning, statements are mapped to intents, and the chabot can handle infinite variations of the same statement. Why is this technique useful? The following example cites this:

Consider you want to ask the contact details of a company. Now every person will ask it in a different way. Following are few of the variations that are possible:

  • Please share your contact details
  • Where do you reside
  • Where are you located
  • Can you share your location
  • Please provide your address
Now the intent of the above questions is “asking address”. So a answer can be mapped to the intent, rather than all the questions. This helps in managing the chatbot easy.

Before intent recognition came into existence, most popular being Wit.ai, recently acquired by Facebook, earlier chatbots were purely based on Artificial Intelligence Markup Language (AIML). AIML was purely a question-answering template. A lot of manual intervention was needed to make the chatbot sound intelligent. Based on pure pattern matching in the questions, it had a basic flaw - it was not able to handle the infinite variations in a question.

Context identification is still a difficult problem, and one of the core problems the AI community is working on. It is argued that, once this problem is solved, AI might have reached the Artificial General Intelligence level, which means the chatbot will be able to match up to human capabilities.

Intent recognition engines such as Wit.ai, Watson Conversation and Microsoft Bot Framework have changed the chatbot scene. Today it is very easy for anyone to develop a chatbot by training it on various intents. Context identification is handled by such engines using a manual process of creating stories or dialogues. A person who is training the chatbot has to write various dialogue flows to make the chatbot understand context.

We at Cere Labs, a Mumbai based startup in Artificial Intelligence, have developed successful chatbots that uses intent recognition. After training the chatbots on various intents, the chatbots have started sounding intelligent, and are able to answer most of the user’s questions. Look into this space as we elaborate on the process of intent recognition.

Let your users talk to your website by using chatbots. You can talk to our chatbot on Cere Lab’s facebook page - https://www.facebook.com/cerelabs/

Comments

  1. We can build your chatbot from scratch, instilling the best practices of a conversational user experience. From setting up a chatbot on your Facebook Page, to implementing analytics so you can understand how users are becoming customers. Malaysia chatbots

    ReplyDelete
  2. This is such a great resource that you are providing and you give it away for free. This is really a nice and informative, containing all information and also has a great impact on the new technology. You wrote something that people could understand and made the subject intriguing for everyone. Really, great blog you have got here.
    Chatbot Development Companies
    Chatbot Companies in UAE
    AI Conversational Chatbots
    Virtual Assistant Chatbot
    AI Chatbot Companies

    ReplyDelete
  3. Thank you for sharing such a useful article. It will be useful to those who are looking for knowledge. Continue to share your knowledge with others through posts like these, and keep posting on
    Big Data Solutions 
    Advanced Data Analytics Services
    Data Modernization Solutions
    AI & ML Service Provider

    ReplyDelete
  4. Great Post! Thanks for your Share.


    Quality Onesie: Kigurumi Onesie Pajamas For Adults And Kids

    Qualityonesie offers Pikachu, Disney, Anime, Hello Kitty, Animals, Winnie the Pooh, Stitch, Unicorn Kigurumi Onesie Pajamas for Adults and Kids.

    ReplyDelete
  5. Play for Real Money with Our Bonus & Enjoy This
    There is 1xbet no カジノ シークレット shortage of free play offers for online casino gambling sites – the casino games you play for real money or for jeetwin free at

    ReplyDelete
  6. I guess I am not the only one having all the enjoyment here keep up the good work. best ai chatbot

    ReplyDelete

Post a Comment

Popular posts from this blog

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe applicati

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. ( http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf )  What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting.  A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. T

Understanding Generative Adverserial Networks - Part 1

This is a two part series on understanding Generative Adversarial Networks (GANs). This part deals with the conceptual understanding of GANs. In the second part we will try to understand the mathematics behind GANs. Generative networks have been in use for quite a while now. And so have discriminative networks. But only in 2014 did someone get the brilliant idea of using them together. These are the generative adversarial networks. This kind of deep learning model was invented by Ian Goodfellow . When we work with data already labelled, it’s called supervised learning. It’s much easier compared to unsupervised learning, which has no predefined labels, making the task more vague.  "Generative Adversarial Networks is the most interesting idea in the last ten years in Machine Learning." - Yann LeCun In this post, we’ll discuss what GANs are and how they work, at a higher , more abstract level. Since 2014, many variations of the traditional GAN have co