Methods, apparatuses and computer program products for providing a conversational data-to-text system

1. An apparatus comprising at least one processor and at least one non-transitory memory comprising program code, the at least one non-transitory memory and the program code configured to, the at least one processor, cause the apparatus to at least:

receive a first natural language query from a client device;

generate, based at least in part on the first natural language query, one or more semantic frames;

retrieve, based at least in part on the one or more semantic frames, a multi-dimensional dataset comprising one or more multi-dimensional data objects each placed in a feature space of a plurality of feature spaces, wherein each feature space comprises one or more dimensions corresponding to categorical data;

generate, based at least in part on the one or more semantic frames, an expected structure of a resulting multi-dimensional data object and a first analytic operation query to be performed on the resulting multi-dimensional data object, wherein the first analytic operation query defines at least one analytic operation type of a plurality of analytic operation types;

generate, based at least in part on executing the first analytic operation query on the resulting multi-dimensional data object, insights associated with data represented by the resulting multi-dimensional data object;

generate a first natural language response to the first natural language query based at least in part on the insights;

convert the first natural language response from text to audio output; and

transmit the audio output of the first natural language response to the client device.

2. The apparatus of claim 1 , wherein the first analytic operation query further defines at least one query parameter.

3. The apparatus of claim 2 , wherein the at least one query parameter corresponds to a dimension instance in a feature space of the plurality of feature spaces associated with the multi-dimensional dataset.

4. The apparatus of claim 2 , wherein the at least one analytic operation type comprises one or more of a filtering operation, a grouping operation, or a variance operation.

5. The apparatus of claim 3 , wherein the feature space comprises a corresponding measure representing numerical data that a given multi-dimensional data object represents.

6. The apparatus of claim 1 , wherein the at least one non-transitory memory and the program code configured to, with the at least one processor, cause the apparatus to further:

select a narrative function script based at least in part on the first natural language query; and

generate the first natural language response further based at least in part on the narrative function script.

7. The apparatus of claim 1 , wherein, subsequent to transmitting the first natural language response to the client device, the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to further:

update contextual data stored in a discourse model based at least in part on the first natural language query and the first natural language response;

receive a second natural language query from the client device;

generate, based at least in part on the second natural language query and the contextual data, a second analytic operation query associated with the multi-dimensional dataset;

generate, based at least in part on the multi-dimensional dataset and executing the second analytic operation query, a second multi-dimensional data object;

generate a second natural language response to the second natural language query based at least in part on the second multi-dimensional data object; and

transmit the second natural language response to the client device.

8. The apparatus of claim 7 , wherein, when generating the second analytic operation query, the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to further:

generate at least one inferred query parameter based at least in part on the second natural language query and the contextual data.

9. The apparatus of claim 8 , wherein, when generating the second multi-dimensional data object, the at least one non-transitory memory and the program code are configured to, with the at least one processor, cause the apparatus to further:

generate the second multi-dimensional data object based at least in part on the at least one inferred query parameter.

10. A computer-implemented method comprising:

receiving a first natural language query from a client device;

generating, based at least in part on the first natural language query, one or more semantic frames;

retrieving, based at least in part on the one or more semantic frames, a multi-dimensional dataset comprising one or more multi-dimensional data objects each placed in a feature space of a plurality of feature spaces, wherein each feature space comprise one or more dimensions corresponding to categorical data;

generating, based at least in part on the one or more semantic frames, an expected structure of a resulting multi-dimensional data object and a first analytic operation query to be performed on the resulting multi-dimensional data object, wherein the first analytic operation query defines at least one analytic operation type of a plurality of analytic operation types;

generating, based at least in part on executing the first analytic operation query on the resulting multi-dimensional data object, insights associated with data represented by the resulting multi-dimensional data object;

generating a first natural language response to the first natural language query based at least in part on the insights;

converting the first natural language response from text to audio output; and

transmitting the audio output of the first natural language response to the client device.

11. The computer-implemented method of claim 10 , wherein the first analytic operation query further defines at least one query parameter.

12. The computer-implemented method of claim 11 , wherein the at least one query parameter corresponds to a dimension instance in a feature space of the plurality of feature spaces associated with the multi-dimensional dataset.

13. The computer-implemented method of claim 11 , wherein the at least one analytic operation type comprises one or more of a filtering operation, a grouping operation, or a variance operation.

14. The computer-implemented method of claim 11 , wherein the feature space comprises a corresponding measure representing numerical data that a given multi-dimensional data object represents.

15. The computer-implemented method of claim 10 , further comprising:

selecting a narrative function script based at least in part on the first natural language query; and

generating the first natural language response further based at least in part on the narrative function script.

16. The computer-implemented method of claim 10 , wherein, subsequent to transmitting the first natural language response to the client device, the computer-implemented method further comprises:

updating contextual data stored in a discourse model based at least in part on the first natural language query and the first natural language response;

receiving a second natural language query from the client device;

generating, based at least in part on the second natural language query and the contextual data, a second analytic operation query associated with the multi-dimensional dataset;

generating a second multi-dimensional data object based at least in part on the multi-dimensional dataset and executing the second analytic operation query;

generating a second natural language response to the second natural language query based at least in part on the second multi-dimensional data object; and

transmitting the second natural language response to the client device.

17. The computer-implemented method of claim 16 , wherein generating the second analytic operation query further comprises:

generating at least one inferred query parameter based at least in part on the second natural language query and the contextual data.

18. The computer-implemented method of claim 17 , wherein generating the second multi-dimensional data object is further based at least in part on the at least one inferred query parameter.

19. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to:

receive a first natural language query from a client device;

generate, based at least in part on the first natural language query, one or more semantic frames;

retrieve, based at least in part on the one or more semantic frames, a multi-dimensional dataset comprising one or more multi-dimensional data objects each placed in a feature space of a plurality of feature spaces, wherein each feature space comprises one or more dimensions corresponding to categorical data;

generate, based at least in part on the one or more semantic frames, an expected structure of a resulting multi-dimensional data object and a first analytic operation query to be performed on the resulting multi-dimensional data object, wherein the first analytic operation query defines at least one analytic operation type of a plurality of analytic operation types;

generate, based at least in part on executing the first analytic operation query on the resulting multi-dimensional data object, insights associated with data represented by the resulting multi-dimensional data object;

generate a first natural language response to the first natural language query based at least in part on the insights;

convert the first natural language response from text to audio output; and

transmit the audio output of the first natural language response to the client device.

20. The computer program product of claim 19 , wherein the first analytic operation query defines at least one query parameter.

21. The computer program product of claim 20 , wherein the at least one query parameter corresponds to a dimension instance in a feature space of the plurality of feature spaces associated with the multi-dimensional dataset.

22. The computer program product of claim 20 , wherein the at least one analytic operation type comprises one or more of a filtering operation, a grouping operation, or a variance operation.

23. The computer program product of claim 20 , wherein the feature space comprises a corresponding measure representing numerical data that a given multi-dimensional data object represents.

24. The computer program product of claim 19 , wherein, the computer-readable program code portions comprise the executable portion configured to:

select a narrative function script based at least in part on the first natural language query; and

generate the first natural language response based at least in part on the narrative function script.

25. The computer program product of claim 19 , wherein, subsequent to transmitting the first natural language response to the client device, the computer-readable program code portions further comprise the executable portion configured to:

update contextual data stored in a discourse model based at least in part on the first natural language query and the first natural language response;

receive a second natural language query from the client device;

generate, based at least in part on the second natural language query and the contextual data, a second analytic operation query associated with the multi-dimensional dataset;

generate, based at least in part on the multi-dimensional dataset and executing the second analytic operation query, a second multi-dimensional data object;

generate a second natural language response to the second natural language query based at least in part on the second multi-dimensional data object; and

transmit the second natural language response to the client device.

26. The computer program product of claim 25 , wherein, when generating the second analytic operation query, the computer-readable program code portions comprise the executable portion configured to:

generate at least one inferred query parameter based at least in part on the second natural language query and the contextual data.

27. The computer program product of claim 26 , wherein generating the second multi-dimensional data object is further based at least in part on the at least one inferred query parameter.