Representation learning for tax rule bootstrapping
A rule having text is pre-processed by replacing terms with dummy tokens. A first machine learning model (MLM) uses the dummy tokens to generate a dependency graph with nodes related by edges tagged with dependency tags. A second MLM uses the dependency graph to generate a canonical version with node labels. The node labels are sorted into a lexicographic order to form a document. A third MLM uses the document to generate a machine readable vector (MRV) that embeds the document as a sequence of numbers representative of a structure of the rule. The MRV is compared to additional MRVs corresponding to additional rules for which computer useable program code blocks have been generated. A set of MRVs is identified that match the MRV within a range. The set of MRVs correspond to a set of rules from the additional rules. The set of rules is displayed to a user.
1. A method comprising:
receiving a rule comprising text;
pre-processing the rule by replacing terms in the rule with a plurality of dummy tokens denoting a plurality of entities;
generating, using a first machine learning model which takes the plurality of dummy tokens as input, a dependency graph comprising a rooted tree having a plurality of nodes related by a plurality of edges that are tagged according to a plurality of dependency tags;
generating, using a second machine learning model which takes the dependency graph as input, a canonical version of the dependency graph, wherein the canonical version comprises a canonical graph having a plurality of node labels;
sorting the plurality of node labels into a lexicographic order to form a document;
generating, using a third machine learning model which takes the document as input, a machine readable vector that embeds the document as a sequence of numbers representative of a structure of the rule;
comparing the machine-readable vector to a plurality of additional machine readable vectors, wherein the plurality of additional machine readable vectors corresponds to a plurality of additional rules for which a plurality of computer useable program code blocks has been generated;
identifying a set of machine readable vectors, from the plurality of additional machine readable vectors, that match the machine readable vector within a range, wherein the set of machine readable vectors correspond to a set of rules from the plurality of additional rules; and
displaying the set of rules to a user.
2. The method of claim 1 , further comprising:
displaying, to the user, a group of computer useable program code blocks corresponding to the set of rules.
3. The method of claim 2 , further comprising:
generating new computer useable program code configured to implement the rule in a computer, wherein generating comprises re-using at least some of the group of computer useable program code blocks for the set of rules.
4. The method of claim 1 , wherein comparing comprises:
performing a nearest neighbor retrieval between the machine readable vector and the plurality of additional machine readable vectors over a dimensional space of the machine readable vector.
5. The method of claim 1 , further comprising:
clustering the machine readable vector with the plurality of additional machine readable vectors to form a Voronoi diagram;
calculating a plurality of distances using the Voronoi diagram, wherein each distance represents a similarity of a structure of a rule in the set to a corresponding structure of the rule comprising text;
displaying, to the user, the computer useable program code for the set of rules together with the plurality of distances.
6. The method of claim 1 , wherein the rule comprises a natural language rule, and wherein pre-processing comprises natural language processing.
7. The method of claim 1 , wherein the first machine learning model comprises a natural language processing machine learning model.
8. The method of claim 1 , wherein the second machine learning model comprises a Weisfeiler-Lehman (WL) algorithm, and wherein the generating the canonical version of the dependency graph comprises continuously executing the WL algorithm until all node labels converge.
9. The method of claim 1 , wherein the third machine learning model comprises an unsupervised machine learning model trained to convert the document to the machine readable vector.
10. A system comprising:
a data repository storing a rule, a plurality of dummy tokens representing a plurality of entities in the rule, a dependency graph comprising a rooted tree having a plurality of nodes related by a plurality of edges tagged according to a plurality of dependency tags, a canonical graph having a plurality of node labels, a document formed from the plurality of node labels, a machine readable vector which embeds the document as a sequence of numbers representative of a structure of the rule, and a plurality of additional machine readable vectors representative of a plurality of structures of a plurality of additional rules;
a pre-processing engine configured to pre-process the rule by replacing terms in the rule with the plurality of dummy tokens;
a document generator configured to sort the plurality of node labels into a lexicographic order to form the document; and
a machine learning model execution engine configured to execute:
a first machine learning model which receives as input the plurality of dummy tokens and outputs the dependency graph,
a second machine learning model which receives as input the dependency graph and outputs the canonical graph, and
a third machine learning model which receives as input the document and outputs the machine readable vector;
a comparator configured to:
compare the machine readable vector to the plurality of additional machine readable vectors; and
identify a set of machine readable vectors, from the plurality of additional machine readable vectors, that match the machine readable vector within a range; and
a display device configured to display a set of rules that correspond to the set of machine readable vectors.
11. The system of claim 10 , wherein the data repository further comprises a plurality of computer useable program code blocks corresponding to the plurality of additional rules, and wherein the display device is further configured to display a group of computer useable program code blocks corresponding to the set of rules.
12. The system of claim 11 , further comprising:
a code generator configured to generate new computer useable program code configured to implement the rule in a computer by re-using at least some of the group of computer useable program code blocks for the set of rules.
13. The system of claim 10 wherein:
the first machine learning model comprises a natural language processing machine learning model,
the second machine learning model comprises a Weisfeiler-Lehman (WL) algorithm, and
the third machine learning model comprises an unsupervised machine learning model trained to convert the document to the machine readable vector.
14. A non-transitory computer readable storage medium comprising computer readable program code, the computer readable program code for causing a computer system to:
receive a rule comprising text;
pre-process the rule by replacing terms in the rule with a plurality of dummy tokens denoting a plurality of entities;
generate, using a first machine learning model which takes the plurality of dummy tokens as input, a dependency graph comprising a rooted tree having a plurality of nodes related by a plurality of edges that are tagged according to a plurality of dependency tags;
generate, using a second machine learning model which takes the dependency graph as input, a canonical version of the dependency graph, wherein the canonical version comprises a canonical graph having a plurality of node labels;
sort the plurality of node labels into a lexicographic order to form a document; and
generate, using a third machine learning model which takes the document as input, a machine readable vector that embeds the document as a sequence of numbers representative of a structure of the rule;
compare the machine-readable vector to a plurality of additional machine readable vectors, wherein the plurality of additional machine readable vectors corresponds to a plurality of additional rules for which a plurality of computer useable program code blocks has been generated;
identify a set of machine readable vectors, from the plurality of additional machine readable vectors, that match the machine readable vector within a range, wherein the set of machine readable vectors correspond to a set of rules from the plurality of additional rules; and
display the set of rules to a user.
15. The non-transitory computer readable storage medium of claim 14 , wherein the computer readable program code is further for causing the computer system to:
display, to the user, a group of computer useable program code blocks corresponding to the set of rules.
16. The non-transitory computer readable storage medium of claim 15 , wherein the computer readable program code is further for causing the computer system to:
generate new computer useable program code configured to implement the rule in a computer, wherein generating comprises re-using at least some of the group of computer useable program code blocks for the set of rules.
17. The non-transitory computer readable storage medium of claim 14 , wherein the computer readable program code is further for causing the computer system to:
perform a nearest neighbor retrieval between the machine readable vector and the plurality of additional machine readable vectors over a dimensional space of the machine readable vector.
18. The non-transitory computer readable storage medium of claim 14 , wherein the computer readable program code is further for causing the computer system to:
cluster the machine readable vector with the plurality of additional machine readable vectors to form a Voronoi diagram;
calculate a plurality of distances using the Voronoi diagram, wherein each distance represents a similarity of a structure of a rule in the set to a corresponding structure of the rule comprising text;
display, to the user, the computer useable program code for the set of rules together with the plurality of distances.
19. The non-transitory computer readable storage medium of claim 14 , wherein the rule comprises a natural language rule, and wherein the computer readable program code for pre-processing comprises computer readable program code for performing natural language processing.
20. The non-transitory computer readable storage medium of claim 14 , wherein:
the first machine learning model comprises a natural language processing machine learning model;
the second machine learning model comprises a Weisfeiler-Lehman (WL) algorithm, and wherein the generating the canonical version of the dependency graph comprises continuously executing the WL algorithm until all node labels converge; and
the third machine learning model comprises an unsupervised machine learning model trained to convert the document to the machine readable vector.