Intent authoring using weak supervision and co-training for automated response systems

A system for training automated response systems using weak supervision and co-training, comprising:

• a processor executing instructions stored in a memory device;
• wherein the processor:
• receives a plurality of conversational logs comprising interactive dialog sessions between agents and clients for a given product or service;
• retrieves a subset of the plurality of conversational logs according to a defined criterion, wherein the subset of the plurality of conversational logs is retrieved in response to an input query by a user such that the defined criterion comprises one or more utterances relevant to the semantic scope of intent input by the user during the input query;
• receives input of the user labeling a selected set of the subset of the plurality of retrieved conversational logs, the labeling associated with a semantic scope of intent considered by the clients;
• and
• applies a combination of propagation operations and learning algorithms using the selected set of labeled conversational logs to a remaining corpus of the plurality of conversational logs to train the automated response system according to the semantic scope of intent, wherein applying the combination of propagation operations and learning algorithms further comprises:
• defining the labels by the user for the selected set of the subset of conversational logs comprising retrieved utterances to the subset of conversational logs according to the input query;
• training a probabilistic classifier using the defined labels of features of the retrieved utterances;
• wherein the probabilistic classifier produces labeling decisions for the subset of conversational logs;
• weighting the features of the retrieved utterances in a model optimization process;
• and
• training an additional classifier using the weighted features of the retrieved utterances and applying the additional classifier to the remaining corpus.

A computer program product for training automated response systems using weak supervision and co-training, by a processor, the computer program product embodied on a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

• an executable portion that receives a plurality of conversational logs comprising interactive dialog sessions between agents and clients for a given product or service;
• an executable portion that retrieves a subset of the plurality of conversational logs according to a defined criterion, wherein the subset of the plurality of conversational logs is retrieved in response to an input query by a user such that the defined criterion comprises one or more utterances relevant to the semantic scope of intent input by the user during the input query;
• an executable portion that receives input of the user labeling a selected set of the subset of the plurality of retrieved conversational logs, the labeling associated with a semantic scope of intent considered by the clients;
• and
• an executable portion that applies a combination of propagation operations and learning algorithms using the selected set of labeled conversational logs to a remaining corpus of the plurality of conversational logs to train the automated response system according to the semantic scope of intent, wherein applying the combination of propagation operations and learning algorithms further comprises:
• defining the labels by the user for the selected set of the subset of conversational logs comprising retrieved utterances to the subset of conversational logs according to the input query;
• training a probabilistic classifier using the defined labels of features of the retrieved utterances;
• wherein the probabilistic classifier produces labeling decisions for the subset of conversational logs;
• weighting the features of the retrieved utterances in a model optimization process;
• and
• training an additional classifier using the weighted features of the retrieved utterances and applying the additional classifier to the remaining corpus.