Triggering event identification and application dialog validation

1. A computer-implemented method comprising:

receiving, from a client device associated with a group-based communication system, user interaction data associated with the group-based communication system;

inputting the user interaction data to a machine learning model, wherein the machine learning model is generated based at least in part on historical triggering event identification data, wherein the machine learning model is trained on first data that includes a labeled user representing a user profile associated with an application dialog data and on second data that includes the user profile associated with a type of the user interaction data, wherein the machine learning model includes a normalization function to parse the user interaction data into data units;

receiving, from the machine learning model, a triggering event associated with an application external to the group-based communication system;

sending an application dialog request, associated with the triggering event, to the application;

in response to sending the application dialog request to the application, receiving an application dialog from the application, wherein the application dialog includes instructions for rendering, via a group-based communication user interface associated with the group-based communication system, a group-based message including a template associated with the application; and

based at least in part on a determination that the application is validated for communication with the client device, outputting the application dialog to the client device for display via the group-based communication user interface.

2. The computer-implemented method of claim 1 , wherein the user interaction data is associated with at least one of a message, an input, an upload, or another user engagement with the group-based communication user interface.

3. The computer-implemented method of claim 1 , wherein the triggering event is stored with the historical triggering event identification data by the group-based communication system for training the machine learning model and updating a trigger event registry.

4. The computer-implemented method of claim 1 , further comprising identifying the triggering event from within the user interaction data based at least in part on parsing the user interaction data.

5. The computer-implemented method of claim 1 , further comprising comparing the user interaction data to a trigger event registry to identify the triggering event.

6. The computer-implemented method of claim 5 , further comprising determining an application address associated with the application and the triggering event, wherein the application dialog request is sent to the application address.

7. The computer-implemented method of claim 1 , wherein the application dialog request is associated with at least one of a trigger token, an indication of the application dialog, or an application dialog content indicator.

8. The computer-implemented method of claim 1 , wherein the application dialog, when received from the application, is associated with dialog validation data.

9. The computer-implemented method of claim 8 , wherein the determination that the application is validated is based at least in part on:

comparing the dialog validation data to one or more validation parameters; and

determining that the dialog validation data satisfies the one or more validation parameters.

10. The computer-implemented method of claim 9 , wherein the application dialog request is associated with a first time stamp, the dialog validation data is associated with a second time stamp, and wherein the one or more validation parameters comprise a response time threshold, the computer-implemented method further comprising:

comparing the response time threshold to a time period between the second time stamp and the first time stamp; and

determining that the application is validated based at least in part on the time period between the second time stamp and the first time stamp being within the response time threshold.

11. A system comprising:

one or more processors; and

one or more non-transitory storing computer-readable instructions that, when executed by the one or more processors, cause the system to perform operations comprising:

receiving, from a client device associated with a group-based communication system, user interaction data associated with the group-based communication system;

inputting the user interaction data to a machine learning model, wherein the machine learning model is generated based at least in part on historical triggering event identification data, wherein the machine learning model is trained on first data that includes a labeled user representing a user profile associated with an application dialog data and on second data that includes the user profile associated with a type of the user interaction data;

receiving, from the machine learning model, a triggering event associated with an application external to the group-based communication system;

sending an application dialog request, associated with the triggering event, to the application;

in response to sending the application dialog request to the application, receiving an application dialog and dialog validation data from the application, wherein the application dialog includes instructions for rendering, via a group-based communication user interface associated with the group-based communication system, a group-based message including a template associated with the application; and

based at least in part on a determination, using the dialog validation data, that the application is validated for communication with the client device, outputting the application dialog to the client device for display via the group-based communication user interface.

12. The system of claim 11 , wherein the user interaction data is associated with at least one of a message, an input, an upload, or another user engagement with the group-based communication user interface, the operations further comprising identifying the triggering event from within the user interaction data based at least in part on parsing the user interaction data.

13. The system of claim 11 , wherein the triggering event is stored with the historical triggering event identification data by the group-based communication system for training the machine learning model and the historical triggering event identification data includes historical user interaction data.

14. The system of claim 11 , the operations further comprising:

comparing the user interaction data to a trigger event registry to identify the triggering event; and

determining an application address associated with the application and the triggering event, wherein the application dialog request is sent to the application address.

15. The system of claim 11 , wherein the application dialog, when received from the application, is associated with the dialog validation data, and wherein the determination that the application is validated is based at least in part on:

comparing the dialog validation data to one or more validation parameters; and

determining that the dialog validation data satisfies the one or more validation parameters.

16. One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving, from a client device associated with a group-based communication system, user interaction data associated with the group-based communication system;

inputting the user interaction data to a machine learning model, wherein the machine learning model is generated based at least in part on historical triggering event identification data, wherein the machine learning model is trained on first data that includes a labeled user representing a user profile associated with an application dialog data and on second data that includes the user profile associated with a type of the user interaction data;

receiving, from the machine learning model, a triggering event associated with an application external to the group-based communication system;

sending an application dialog request, associated with the triggering event, to the application;

in response to sending the application dialog request to the application, receiving an application dialog from the application, wherein the application dialog includes instructions for rendering, via a group-based communication user interface associated with the group-based communication system, a group-based message including a template associated with the application; and

based at least in part on a determination that the application is validated for communication with the client device, outputting the application dialog to the client device for display via the group-based communication user interface.

17. The one or more non-transitory computer-readable media of claim 16 , wherein the user interaction data is associated with at least one of a message, an input, an upload, or another user engagement with the group-based communication user interface, the operations further comprising identifying the triggering event from within the user interaction data based at least in part on parsing the user interaction data.

18. The one or more non-transitory computer-readable media of claim 16 , wherein the triggering event is stored with the historical triggering event identification data by the group-based communication system for training the machine learning model.

19. The one or more non-transitory computer-readable media of claim 16 , the operations further comprising:

comparing the user interaction data to a trigger event registry to identify the triggering event; and

determining an application address associated with the application and the triggering event, wherein the application dialog request is sent to the application address.

20. The one or more non-transitory computer-readable media of claim 16 , wherein the application dialog, when received from the application, is associated with dialog validation data, and wherein the determination that the application is validated is based at least in part on:

comparing the dialog validation data to one or more validation parameters; and

determining that the dialog validation data satisfies the one or more validation parameters.