Embodiments provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method comprising: ingesting, using a rule-based module, a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; matching, using the rule-based module, one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingesting, using a statistical matching module, one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more classifiers using the one or more feature vector representations of the classes; training classification objectives using the one or more feature vector representations of the training examples; and incorporating the training of the classifiers and the classification objectives into enhanced chatflow system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method comprising: ingesting, using a rule-based module, a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; matching, using the rule-based module, one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingesting, using a statistical matching module, one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more classifiers using the one or more feature vector representations of the classes; training classification objectives using the one or more feature vector representations of the training examples; and incorporating the training of the classifiers and the classification objectives into enhanced chatflow system. 2. The method as recited in claim 1, further comprising: ingesting, using the statistical matching module, the expert-designed variations in addition to the usage logs. 3. The method as recited in claim 1, further comprising: expanding, using the rule-based module, the regular expressions and delimiters using synonyms harvested through one or more external data sources. 4. The method as recited in claim 1, further comprising: fixing the rule-based module such that no further alteration to the regular expressions or delimiters is allowed; ingesting, using the statistical matching module, the expert-designed variations in addition to the usage logs; and training, using the statistical matching module, new classes based upon analysis of the expert-designed variations. 5. The method as recited in claim 1, further comprising: ingesting, using the statistical matching module, the expert-designed variations in addition to the usage logs; training, using the statistical matching module, new classes based upon analysis of the expert-designed variations; and replacing the rule-based module with the statistical matching module. 6. The method as recited in claim 1, further comprising: training, using the statistical matching module, the classifiers and the classification objectives using at least one of: linear regression, logistic regression, Multi-Layer-Perceptrons (MLP), and Deep Belief Network (DBN) classifiers. 7. The method as recited in claim 1, further comprising: converting, using the statistical matching module, the classes and training examples into feature vector representations using at least one of: directly combining word vectors, convolutional neural networks to transform variable length sentences to fixed-length feature vector representations, or sentence-to-vector encoders trained on general or domain specific corpora. 8. A computer program product for enhanced chatflow, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: ingest, using a rule-based module, a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; match, using the rule-based module, one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingest, using a statistical matching module, one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designate the node as a class; store the node in a dialog node repository; designate each of the at least one variations as training examples for the designated class; convert the classes and the training examples into feature vector representations; train one or more classifiers using the one or more feature vector representations of the classes; train classification objectives using the one or more feature vector representations of the training examples; and incorporate the training of the classifiers and the classification objectives into enhanced chatflow system. 9. The computer program product as recited in claim 8, wherein the processor is further caused to: ingest, using the statistical matching module, the expert-designed variations in addition to the usage logs. 10. The computer program product as recited in claim 8, wherein the processor is further caused to: expand, using the rule-based module, the regular expressions and delimiters using synonyms harvested through one or more external data sources. 11. The computer program product as recited in claim 8, wherein the processor is further caused to: fix the rule-based module such that no further alteration to the regular expressions or delimiters is allowed; ingest, using the statistical matching module, the expert-designed variations in addition to the usage logs; and train, using the statistical matching module, new classes based upon analysis of the expert-designed variations. 12. The computer program product as recited in claim 8, wherein the processor is further caused to: ingest, using the statistical matching module, the expert-designed variations in addition to the usage logs; train, using the statistical matching module, new classes based upon analysis of the expert-designed variations; and replace the rule-based module with the statistical matching module. 13. The computer program product as recited in claim 8, wherein the processor is further caused to: train, using the statistical matching module, the classifiers and the classification objectives using at least one of: linear regression, logistic regression, Multi-Layer-Perceptrons (MLP), and Deep Belief Network (DBN) classifiers. 14. The computer program product as recited in claim 8, wherein the processor is further caused to: convert, using the statistical matching module, the classes and training examples into feature vector representations using at least one of: directly combining word vectors, convolutional neural networks to transform variable length sentences to fixed-length feature vector representations, or sentence-to-vector encoders trained on general or domain specific corpora. 15. An enhanced chatflow system, comprising: an enhanced chatflow processor configured to: ingest, using a rule-based module, a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; match, using the rule-based module, one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingest, using a statistical matching module, one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designate the node as a class; store the node in a dialog node repository; designate each of the at least one variations as training examples for the designated class; convert the classes and the training examples into feature vector representations; train one or more classifiers using the one or more feature vector representations of the classes; train classification objectives using the one or more feature vector representations of the training examples; and incorporate the training of the classifiers and the classification objectives into enhanced chatflow system. 16. The system as recited in claim 15, wherein the enhanced chatflow processor is further configured to: ingest, using the statistical matching module, the expert-designed variations in addition to the usage logs. 17. The system as recited in claim 15, wherein the enhanced chatflow processor is further configured to: expand, using the rule-based module, the regular expressions and delimiters using synonyms harvested through one or more external data sources. 18. The system as recited in claim 15, wherein the enhanced chatflow processor is further configured to: fix the rule-based module such that no further alteration to the regular expressions or delimiters is allowed; ingest, using the statistical matching module, the expert-designed variations in addition to the usage logs; and train, using the statistical matching module, new classes based upon analysis of the expert-designed variations. 19. The system as recited in claim 15, wherein the enhanced chatflow processor is further configured to: ingest, using the statistical matching module, the expert-designed variations in addition to the usage logs; train, using the statistical matching module, new classes based upon analysis of the expert-designed variations; and replace the rule-based module with the statistical matching module. 20. The system as recited in claim 15, wherein the enhanced chatflow processor is further configured to: train, using the statistical matching module, the classifiers and the classification objectives using at least one of: linear regression, logistic regression, Multi-Layer-Perceptrons (MLP), and Deep Belief Network (DBN) classifiers.