Personality Chat makes it easy to add small talk capabilities to your chatbot. Small talk/chit-chat helps to make chatbots more conversational and personable. This package has more than a 100 scenarios of small talk in the voice of three personas - professional, friendly, humorous. Choose the persona that most closely resembles your chatbot's voice. Given a user query or message, Personality chat tries to match it with the closest scenario using deep learnt similarity models and lexical features. Personality chat also provides editorialy curated responses for each scenario based on the selected persona. See a quick tutorial and overview video here.
If interested in participating in a private preview or access to higher throttling limits, please contact [email protected].
| User query | Professional | Friendly | Humorous |
|---|---|---|---|
| Thank you | You're quite welcome. | You bet. | No prob. |
| Will you marry me? | I think it's best if we stick to a professional relationship. |
You're three-dimensional. I'm non-dimensional. Our love can never be. |
Sure. Take me to City Hall. See what happens. |
| Who made you? | There wouldn't be time to list everyone. | So many people! | Nerds. |
check out the "nodejs/samples" folder for an example
Directions for Using Personality Chat Datasets- Personality chat dataset containing responses and few representative queries for facilitating building your own models using LUIS or QNAMaker. Please note that the scenario matching models provided by the PersonalityChat API has been developed using deep models trained on large number of editorial queries and grammars. Using datasets directly won't include the rich query understanding capabilities provided by the PersonalityChat API.
Personality Chat matches the user's small talk query with a small talk scenario. It does query understanding (QU) to lexically and semantically match a user query. All matched scenarios are returned along with the confidence score. If no scenario is matched, no response is returned. Additionally is also checks for a few indicators to determine how to respond.
isChatQuery: Does the query look like a chitchat/small talk query rather than a real user question?
Check out the Editorial Scenarios and sample query for Personality Chat
We enforce throttling limits on the Personality Chat API at the rate of 30 queries per minute. Throttling is done based on the IP address.
If you are throttled, the API returns a 429 HTTP response with the message Rate limit is exceeded. Try again in X seconds.
Personality Chat also includes a component that is capable of generating responses real-time without editorial content. It uses a Deep Neural Network to generate answers to a chitchat query. This is available as a demo playground chat in Microsoft Cognitive Labs for a restricted set of query intents.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.