International Business Machines Corporation

Kshitij P Fadnis

Chulaka Gunasekara

Kartik Talamadupula

John Michael Boyer

Achille Belly Fokoue-Nkoutche

Jatin Ganhotra

Lazaros Polymenakos

Dinesh Raghu

Pavan Kapanipathi Bangalore

Tathagata Chakraborti

Eli M Dow

Luis Alfonso Lastras-Montano

David Nahamoo

Sachindra Joshi

Biplav Srivastava

Maria Chang

Murray Scott Campbell

Ibrahim Abdelaziz

David Echeverria Ciaurri

Song Feng

Mayank Agarwal

Nathaniel Mills

Bassem Makni

Justin A Ziniel

Collin James Murray

Veronika Thost

Nicholas Mattei

Avinash Balakrishnan

Sunil Davangere Shashidhara

Siva Sankalp Patel

Jorge J Barroso Carmona

Borja Godoy

A method, computer system, and a computer program for conducting a conversational search. In one embodiment, the method includes monitoring a dialogue involving at least one user and capturing user utterances provided during the dialogue. These user utterances are then analyzed and classified according to the context of the dialogue. The dialogue is intervened upon the determination that a user needs additional information and/or upon execution of an action on behalf of the user and based on the plurality of user utterances and context. The Required information may be provided back to the user using Documentation Recommendation Module. The Documentation Recommendation Module determines a valid resource recommendation as determined by a combination of the context and a resource that includes additional information.

Techniques for predicting a personalized URL document to assist a conversation

Techniques are provided for reinforcement learning software agents enhanced by external data. A reinforcement learning model supporting the software agent may be trained based on information obtained from one or more knowledge stores, such as online forums. The trained reinforcement learning model may be tested in an environment with limited connectivity to an external environment to meet performance criteria. The reinforcement learning software agent may be deployed with the tested and trained reinforcement learning model within an environment to autonomously perform actions to process requests.

Data augmented training of reinforcement learning software agent

A computer system adapts processing of expressions by a command-line interface. An expression provided to the command-line interface is analyzed, wherein the command line interface includes pre-defined expression processing. One or more artificial intelligence agents are selected from a plurality of artificial intelligence agents based on the analysis of the expression. The expression is evaluated by the selected one or more artificial intelligence agents to determine processing modifications for the pre-defined expression processing. The expression is processed in accordance with the determined processing modifications and results are provided to the command-line interface. Embodiments of the present invention further include a method and program product for adapting processing of expressions by a shell in substantially the same manner described above.

Flexible artificial intelligence agent infrastructure for adapting processing of a shell

A method for assigning weights to a knowledge graph includes extracting information from a knowledge graph. The information including entities extracted from nodes of the knowledge graph and relations extracted from edges of the knowledge graph. A shortest path generator receives the extracted entities and relations, and potential assigned weights from a heuristic data repository. Weights for the edges of the knowledge graph are determined. The weights are assigned to the edges of the knowledge graph.

Interpretable knowledge contextualization by re-weighting knowledge graphs

One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Infusing knowledge into natural language processing tasks using graph structures

Embodiments relate to a system, program product, and method directed at natural language (NL) and a virtual dialog platform. An NL statement is detected and analyzed to identify one or more entities expressed in the statement. The identified entities are leveraged to parse the statement into keywords. The intent of the received statement is represented as a relationship between two or more of the keywords. A knowledge representation is identified to represent the statement with respect to a formatted module having two or more components and a component relationship structure. Each statement keyword is assigned to a designated module component based on an alignment of the component relationship with the keyword relationship. The statement intent is expressed based on the relationship between the keywords, and a statement response is inferred. The inferred statement is communicated to the virtual dialog platform.

Conversational agents over domain structured knowledge

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

A method, program product and computer system to predict utterances in a dialog system includes receiving a set of utterances associated with a dialog between a client device and a dialog system, mapping the utterances to vector representations of the utterances, and identifying at least one cluster to which the utterances belong from among a plurality of possible clusters. A next cluster is predicted based upon a conditional probability of the next cluster following a set of a predetermined number of previous clusters using a language model. A next utterance is predicted from among a plurality of possible utterances within the predicted next cluster.

Quantized dialog language model for dialog systems

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.

Text classifier operation

A mechanism is provided in a data processing system comprising at least one processor and a memory comprising instructions which, when executed by the at least one processor, causes the at least one processor to train a similar passage cognitive system. The mechanism receives a question and answer key for a question answering cognitive system, the question and answer key comprising a list of question and answer specification pairs. Each question is a text string and each answer specification references one or more text passages from a corpus of information. The mechanism uses the question and answer key to generate a similar passage map for the similar passage cognitive system, the similar passage map comprising a list of text relation pairs. Each text relation pair comprises a sample input text component and a list comprising one or more sample output text components. The mechanism trains a similar passage machine learning model of the similar passage cognitive system using the similar passage map.

Training a similar passage cognitive system using ground truth from a question answering cognitive system

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Dublin, OH, United States of America

Kshitij P Fadnis