International Business Machines Corporation

Nuance Communications, Inc.

Pryon Incorporated

University of Michigan

Tufts College

Intenrational Business Machines Corporation

David Nahamoo

Dimitri Kanevsky

Michael Alan Picheny

Jerome R Bellegarda

Ponani S Gopalakrishnan

Lazaros Polymenakos

Tara N Sainath

Bhuvana Ramabhadran

Lalit R Bahl

Raimo Bakis

Krishna Sundaram Nathan

Vaibhava Goel

Igor Roditis Jablokov

Etienne Marcheret

Steven John Rennie

Dhandapani Shanmugam

Liam David Comerford

Mukund Padmanabhan

Jatin Ganhotra

Chulaka Gunasekara

Marie Wenzel Meteer

Victor S Moore

Stephane Herman Maes

Lior Horesh

Sara H Basson

Jan Kleindienst

Soonthorn Ativanichayaphong

Alexander Faisman

Peter V De Souza

Edward Adam Epstein

Roberto Sicconi

Mahesh Viswanathan

Hagai Aronowitz

Ron Hoory

Jan Sedivy

Cheng Wu

Daniel Mark Coffman

Martin Labsky

Jason William Pelecanos

Tomas Macek

Jan Curin

Clifford A Pickover

Jian Wang

Hao Chen

Pamela Ann Nesbitt

Qi Cheng Li

Wendi L Nusbickel

William F Ganong, Iii

Shao Chun Li

Sachindra Joshi

Ya Bin Dang

Li Jun Mei

Raya Horesh

Siddique M Adoni

Amir Geva

Rangachari Anand

Shay Ben-David

Ladislav Kunc

Rick A Hamilton, Ii

Vittorio R Castelli

Jie Ma

Xin Zhou

Chai Wah Wu

Rui Zhang

Genady Grabarnik

Wlodek Wlodzimierz Zadrozny

Ganesh Nachiappa Ramaswamy

David M Lubensky

Gregory Pelton

Pankaj Dhoolia

Ramesh Ambat Gopinath

Rahul P Akolkar

George Andrei Saon

Mark Edward Epstein

Hong-Kwang Jeff Kuo

Brian E D Kingsbury

Ramesh Natarajan

Abraham P Ittycheriah

Christopher Wildsmith

Sreeram Viswanath Balakrishnan

David Carl Frank

Robert L Mercer

Apostol Ivanov Natsev

Osamuyimen Stewart

Stanley S Chen

Stephen Eric Levy

Salim Estephan Roukos

Charles Francis Wiecha

Bing Hua Zhao

Harshit Kumar

Haim Avron

Zvi Kons

Dinesh Raghu

Holger Quast

Benoit Maison

Ruhi Sarikaya

Juan Manuel Huerta

Michelle Yoonkyung Kim

Honglak Lee

Real Tremblay

A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database. The method further comprises generating a first answer to said first question, wherein generating said first answer comprises causing said question to traverse a first path through said question-answering system, wherein the first path comprises a knowledge base constructed from content of the document collection using a machine learning BERT model; and causing said question to traverse a second path through said question-answering system to generate a second answer to said question, said second answer differing from said first answer, wherein the second path comprises a structured query generator and the database, the database being distinct from the knowledge base.

Question-answering system for answering relational questions by utilizing two paths where at least one path uses BERT model

Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.

Methods and systems for improved document processing and information retrieval

Methods and systems for determining control inputs to a manufacturing apparatus to manufacture a product are described. A processor may receive model data including initial state data indicating an initial state of an input material, a set of model control inputs, and target measurement data associated with a target product. The processor may learn a causal predictive model based on the target data. Each state of the causal predictive model may be based on an application of the model control inputs on a previous state of the causal predictive model. The processor may compare a final state of the causal predictive model with the target measurement data to determine a difference. The processor may determine, based on the difference, a set of control inputs to be assigned to one or more controls. The one or more controls may define a design of the manufacturing process of an end product.

Free-form production based on causal predictive models

Disclosed are methods, systems, devices, apparatus, media, and other implementations, including a method that includes obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set, with the answer data characterizing concepts associated with the one or more documents, and deriving structured output information for the one or more documents based on the answer data produced in response to performing the Q-A search.

Supervised summarization and structuring of unstructured documents

A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Visual structure of documents in question answering

Machine-implemented assistance is provided in interaction among multiple parties. This assistance can include recording of evidence of communication, and in particular to recording of evidence of language-based communication between humans and machines. Further, machine-implemented facilitation of dispute resolution based on the recorded evidence may be provided.

Recording evidence of communication in human-machine interactions

An intelligent document system provides one or more users, who are recipients of documents, with a way to 'interact' with the documents for example for the purpose of understanding the content of the documents, and taking appropriate action in response to receiving the documents. Interacting with one or more documents can include navigating the documents guided by semantic content of the documents, asking questions that are answered based on the content of the documents. In some examples, the documents are 'dynamic' in that users can manipulate data in the document for example for multiple different views or analyses. In some examples, the documents are augmented semantics and ontology that will allow the user to accurately navigate the document and achieve the natural interfacing they desire.

Intelligent document system

A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A unified contextual graph for use by the conversational system, the unified contextual graph comprising components based on database queries of the conversational system and a user dialog between a user and the conversational system. For each of a set of user utterances produced in a dialog with the conversational system, a semantic meaning representation is determined. The semantic meaning representations are converted to respective sentential concept graphs. The unified contextual graph is updated based on new sentential concept graphs while the dialog with the conversational system progresses.

Semantic representation and realization for conversational systems

Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving a source document, applying one or more pre-processes to the source document to produce contextual information representative of the structure and content of the source document, and transforming the source document, based on the contextual information, to generate a question-and-answer searchable document.

Document pre-processing for question-and-answer searching

Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document, and determining whether one or more pre-determined questions stored in a question-answer cache maintained at the local device matches the query data according to one or more matching criteria. The method further includes obtaining from the question-answer cache, in response to a determination that at least one of the pre-determined questions matches the query data received from the remote device, at least one answer data item, associated with at least one pre-determined question, corresponding to an answer to the question relating to the source content.

Systems and methods for question-and-answer searching using a cache

Embodiments provide a computer implemented method, in a data processing system including a processor and a memory including instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method including: ingesting a corpus of information including at least one user input node corresponding to a user question and at least one variation for each 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 training classifiers using the one or more feature vector representations of the classes; and training classification objectives using the one or more feature vector representations of the training examples.

System and method for enhanced chatflow application

A computer-implemented conversational system framework to perform tasks associated with a client request. A conversation application executing on a hardware processor provides application workflow orchestration, the conversation application receiving a client request and sending one or more application requests based on the application workflow orchestration. A conversation system executing on a hardware processor provides conversation workflow orchestration, the conversation system receiving the one or more application requests. The conversation application and the conversation system develop dialog context and store the dialog context in a memory device. The conversation application and the conversation system develop the dialog context by invoking at least one micro-service to perform tasks associated with the one or more application requests. The conversation application generates a response to the client request based on the developed dialog context.

Conversational framework

A system and method performs automated domain concept discovery and clustering using word embeddings by receiving a set of documents for natural language processing for a domain, representing a plurality of entries in the set of documents as continuous vectors in a high dimensional continuous space, applying a clustering algorithm based on a mutual information optimization criterion to form a set of clusters, associating each entry of the plurality of entries with each cluster in the set of clusters through formalizing an evidence based model of each cluster given each entry, calculating a mutual information metric between each entry and each cluster using the evidence based model, and identifying a nominal center of each cluster by maximizing the mutual information.

Domain concept discovery and clustering using word embedding in dialogue design

A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A user input is received in a dialog between a user and the conversational system, the user input in a natural language. A domain trained semantic matcher is used to determine a set of entities and a user intent from the user input. One or more queries is generated to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge. The results from the one or more queries are ranked based on domain specific knowledge. A system response is presented in the dialog based on at least a highest ranked result from the plurality of knowledge sources.

Implicit dialog approach operating a conversational access interface to web content

Methods and systems for generating output of a simulation model in a simulation system are described. In an example, a processor may retrieve observed output data from a memory. The observed output data may be generated based on a simulation operator of a simulation model. The processor may further optimize a generalization error of a distance measure between the observed output data and model output data. The model output data may be generated based on a high-fidelity operator. The processor may further determine a correction operator based on the optimized generalization error of the distance measure. The processor may further append the correction operator to the simulation operator to produce a supplemented operator. The processor may further generate supplemented output data by applying the simulation model with the supplemented operator on a set of inputs.

Mis-specified model supplementation

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David Nahamoo