CONVERSATIONAL SYNTAX USING CONSTRAINED NATURAL LANGUAGE PROCESSING FOR ACCESSING DATASETS
In general, techniques are described for various aspects of accessing datasets. A device comprising a memory configured to store the dataset, and a processor may be configured to perform the techniques. The processor may expose a language sub-surface specifying a natural language containment hierarchy defining a grammar for a natural language as a hierarchical arrangement of a plurality of language sub-surfaces. The processor may receive a query to access the dataset, the query conforming to a portion of the natural language provided by the exposed language sub-surface. The processor may transform the query into one or more statements that conform to a formal syntax associated with the dataset, access, based on the one or more statements, the dataset to obtain a query result, and output the query result.
1 . A device configured to interpret a multi-dimensional dataset, the device comprising:
a memory configured to store the multi-dimensional dataset; and
one or more processors configured to:
apply a plurality of machine learning models to the multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
determine a correlation of one or more dimensions of the multi-dimensional dataset to the results output by each of the plurality of machine learning models;
select, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models; and
output the result for each of the subset of the plurality of machine learning models.
2 . The device of claim 1 , wherein the one or more processors are configured to output the result as a sentence using plain language.
3 . The device of any combination of claim 1 , wherein the one or more processors are configured to output the result for at least one of the subset of the plurality of machine learning models as a graph identifying a relevance of each of the one or more dimensions to the result for each of the subset of the plurality of machine learning models.
4 . The device of claim 3 , wherein the graph comprises an impact graph.
5 . The device of claim 1 , wherein the one or more processors are configured to output the result for each of the subset of the plurality of machine learning models as a graphical representation of a decision tree.
6 . The device of claim 1 , wherein the one or more processors are further configured to:
determine, based on a comparison of the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models to a relevance threshold, one or more low relevance dimensions of the multi-dimensional dataset that have low relevance to the result output by each of the plurality of machine learning models; and
output an indication explaining that the one or more low relevance dimensions have low relevance to the result.
7 . The device of claim 6 , wherein the one or more processors are configured to output a sentence in plain language that explain the one or more low relevance dimensions having low relevance to the result.
8 . The device of claim 1 , wherein the one or more processors are further configured to refrain from transforming the one or more dimensions of the multi-dimensional dataset prior to application of the plurality of machine learning models.
9 . The device of claim 1 , wherein the one or more processors are further configured to:
determine, based on the results for each of the one or more of the plurality of machine learning models, one or more of a plurality of charts to explain the corresponding result;
rank the one or more of the plurality of charts to identify a highest ranked chart;
select the highest ranked chart; and
output the highest ranked chart as a visual chart.
10 . The device of claim 9 , wherein the one or more processors are further configured to:
generate an explanation in plain language explaining a formulation of the visual chart; and
output the explanation.
11 . The device of claim 1 , wherein the one or more processors are further configured to:
generate a pipeline report explaining how the device produced the plurality of the machine learning models; and
output the pipeline report.
12 . A method of interpreting a multi-dimensional dataset, the method comprising:
applying a plurality of machine learning models to the multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
determining a correlation of the one or more dimensions of the multi-dimensional dataset to the results output by each of the plurality of machine learning models;
selecting, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models; and
outputting the result for each of the subset of the plurality of machine learning models.
13 . The method of claim 12 , wherein outputting the result comprises outputting the result as a sentence using plain language.
14 . The method of claim 12 , wherein outputting the result comprises outputting the result for at least one of the subset of the plurality of machine learning models as a graph identifying a relevance of each of the one or more dimensions to the result for each of the subset of the plurality of machine learning models.
15 . The method of claim 14 , wherein the graph comprises an impact graph.
16 . The method of claim 12 , wherein outputting the result comprises outputting the result for each of the subset of the plurality of machine learning models as a graphical representation of a decision tree.
17 . The method of claim 12 , further comprising:
determining, based on a comparison of the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models to a relevance threshold, one or more low relevance dimensions of the multi-dimensional dataset that have low relevance to the result output by each of the plurality of machine learning models; and
outputting an indication explaining that the one or more low relevance dimensions have low relevance to the result.
18 . The method of claim 17 , wherein outputting the indication comprises outputting a sentence in plain language that explain the one or more low relevance dimensions having low relevance to the result.
19 . The method of claim 12 , further comprising refraining from transforming the one or more dimensions of the multi-dimensional dataset prior to application of the plurality of machine learning models.
20 . A non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors to:
apply a plurality of machine learning models to a multi-dimensional dataset to obtain a result output by each of the plurality of machine learning models;
determine a correlation of the one or more dimensions of the multi-dimensional dataset to the result output by each of the plurality of machine learning models;
select, based on the correlation determined between the one or more dimensions and the result output by each of the plurality of machine learning models, a subset of the plurality of machine learning models to obtain the result for each of the subset of the plurality of machine learning models; and
output the result for each of the subset of the plurality of machine learning models.