International Business Machines Corporation

Dinesh Raghu

Sachindra Joshi

Pankaj Dhoolia

Jitendra Ajmera

Sathish R Indurthi

John Michael Boyer

Kshitij P Fadnis

Dhiraj Madan

Luis Alfonso Lastras-Montano

David Nahamoo

Vibha Singhal Sinha

Sugata Ghosal

Ajay Gupta

Karthik Visweswariah

Nishant Kumar Sinha

Sampath Dechu

Robert Leslie Yates

Danish Contractor

Debdoot Mukherjee

Monika Gupta

Gaurav Pandey

Vikram Tankasali

Vineet Kumar

A domain-specific document is retrieved. The domain-specific document is related to the performance of a process in the domain and includes a plurality of document spans and a plurality of annotations that annotate the spans. A goal-based framework is generated from the domain-specific document. The generation is based on the plurality of annotations and based on the plurality of document spans. The goal-based framework includes entities and entity relationships related to performance of the process. The goal-based framework is transformed into a platform agnostic dialog model based on the entities and the entity relationships. At least one platform specific dialog model is provided to at least one automated response system based on the platform agnostic dialog model.

Assistant dialog model generation

A mechanism is provided for text classifier training. The mechanism receives a training set of text and class specification pairs to be used as a ground truth for training a text classifier machine learning model for a text classifier. Each text and class specification pair comprises a text and a corresponding class specification. A domain terms selector component identifies at least one domain term in the texts of the training set. A domain terms replacer component replaces the at least one identified domain term in the texts of the training set with a corresponding replacement term to form a revised set of text and class specification pairs. A text classifier trainer component trains the text classifier machine learning model using the revised set to form a trained text classifier machine learning model.

Text classifier training

One embodiment provides a method, including: obtaining a plurality of conversational logs; generating a human agent emulator and a user emulator; providing a workspace for a conversational agent, so that an agent designer generates a conversational specification for the conversational agent, wherein the generating a conversational specification comprises: receiving a selection, by the agent designer, of at least one intent for the conversational agent, wherein the receiving a selection is responsive to the conversational agent workspace providing suggestions for intents; providing at least one suggestion for a dialog node that corresponds to the selected at least one intent; and generating a dialog flow for the conversational agent, wherein the generating comprises iteratively receiving, from the agent designer, selection of at least one aspect and receiving at least one selection of the at least one suggestion for dialog nodes; and providing the conversational agent.

Conversational agent generation

Methods, systems, and computer program products for unsupervised learning of interpretable conversation models from conversation logs are provided herein. A computer-implemented method includes obtaining human-to-human conversation logs; training a deep learning model by (i) learning, in an unsupervised manner, semantic labels for dialog contexts in the multiple human-to-human conversation logs, (ii) mapping the learned semantic labels to query responses across the multiple human-to-human conversation logs, and (iii) inferring one or more entities from the multiple conversation logs based at least in part on the mapping; constructing a human-interpretable conversation model based at least in part on patterns determined via the trained deep learning model; and outputting the human-interpretable conversation model to at least one user.

Unsupervised learning of interpretable conversation models from conversation logs

One embodiment provides a method, including: receiving an input from a first user requesting information; generating a conversation model from a dialog that occurs between the user and a human agent; recording the human agent performing an external action required to respond to the input; mapping steps performed during performance of the external action to conversation slots within the dialog; generating an integrated interpretable conversation model comprising a dialog and action script; receiving, at a conversational agent system, a subsequent input from a second user requesting similar information to the information requested by the first user; and providing, by the conversational agent system, a response to the subsequent input, wherein the providing a response comprises the conversational agent system utilizing the integrated interpretable conversational model to replay (i) the dialog and (ii) the action script using the subsequent input.

External action execution with conversational agent

Provided herein is a system, method, and computer program product for modifying a language conversation model of the language learning system. Modifying the language conversation model includes receiving, using a conversational sub-system, voice inputs. The conversational sub-system converts the voice inputs to voice input data and processes the voice input data. The conversational sub-system detects an error in processing the voice input data and, based at least in part on the error, stores additional data comprising additional voice input data in a memory. The conversational sub-system applies machine learning to the additional data to derive a function that is not enabled within the language conversation model. The conversational sub-system develops an update that enables the language conversation model to implement the function. The update is applied to the language conversation model.

Modifying a language conversation model

Methods and arrangements for configuring document editors. A search client add-in is loaded at a document editor. At the document editor, a document is accepted from a search engine. Communication is established between the search client add-in and the search engine. At the search client add-in, supplementary information about the document is received from the search engine. User input about the document is accepted, and is directed to the search engine. The document is informatively enhanced via at least one of: the supplementary information and the user input. Other variants and embodiments are broadly contemplated herein.

Extending document editors to assimilate documents returned by a search engine

A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Knowledge-based editor with natural language interface

A computer-implemented method includes receiving, from a natural language interface system, a natural language task specification, and converting the natural language task specification into a domain independent data flow graph. The data flow graph includes substeps. The method further includes: presenting the data flow graph via the natural language interface system as a natural language program; interactively refining the natural language program; and correspondingly modifying the data flow graph. The computer-implemented method further includes, for each substep: selecting one or more candidate APIs from an API library, based on the substep; interactively narrowing the one or more candidate APIs to at least one selected API; implementing the substep by specifying one or more calls to the at least one selected API to yield a substep implementation; and appending the substep implementation to a result program. A corresponding computer program product and computer system are also disclosed.

Natural language conversation-based programming

A mechanism is provided in a data processing system for text classification. A domain terms selector component, executing on a processor of the data processing system, receives an input text. A domain terms selector component executing on a processor of the data processing system identifies at least one domain term in the input text. A domain terms replacer component executing on a processor of the data processing system replaces the at least one identified domain term in the input text with a corresponding replacement term to form a revised input text. A text classifier component configured with a trained text classifier machine learning model classifies the revised input text to form a class determination.

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New Delhi, India

Dinesh Raghu