International Business Machines Corporation

Lucent Technologies Inc.

Alcatel Lucent

Tin Kam Ho

Luis Alfonso Lastras-Montano

Oded Shmueli

Neil Rohit Mallinar

Rajesh Ramkrishna Bordawekar

Abhishek Shah

Sean A Wilner

Rajendra G Ugrani

Ayush Gupta

Livio B Soares

David Amid

David Boaz

Amir Kantor

Vinith Misra

Bortik Bandyopadhyay

Michele M Franceschini

Robert Leslie Yates

Piotr Mirowski

Henry Spalding Baird

Blake McGregor

Gregory Peter Kochanski

Vadim Sheinin

Lawrence P O'Gorman

Philip Alfred Whiting

Richard Baxter Hull

Daniel Francis Lieuwen

Anwar I Walid

Bharat Kumar

Marina Thottan

Byron H Chen

William Michael MacDonald

Michael R Andrews

Elahe Khorasani

Louis J Lanzerotti

David J Thomson

John D Hobby

Paulo A Santos

Harald Steck

Shivani Deshpande

Ravishankar Palaniappan

Amy Ortega

Yafeng Yin

Robert K Dinoff

Haobo Ren

A method of authoring a conversation service for a chatbot and a database includes receiving, from a user, a selection of a database, and connecting an authoring service of the chatbot to a table in the database; outputting, from the authoring service to the user, a question requesting a description of a subject matter of the table; receiving the description of the subject matter of the table; outputting to the user a question requesting an identification of a key column of the table that contains values that represent the subject matter of the table; receiving the identification of the key column of the table; and translating, by a natural language query service, the description of the subject matter of the table and the key column of the table into the conversation service, wherein the conversation service includes SQL statements suitable for querying the database table.

Authoring a conversation service module from relational data

An embodiment for creating a dialog based on anticipated questions for database driven conversations is provided. The embodiment may include receiving content from a database. The embodiment may also include identifying one or more schemas, one or more entities, and relational data in the content. The embodiment may further include identifying a semantic type and a number of distinct entries for each entity. The embodiment may also include presenting choices for one or more query targets and one or more filtering conditions to a user. The embodiment may further include prompting the user for one or more annotations and one or more clarifying questions for each chosen query target and filtering condition. The embodiment may also include generating a plurality of modular phrases and combining the plurality of modular phrases into one or more sentences and paraphrases of the one or more sentences.

Anticipatory dialog design for question answering with a relational database

Data-parallel ensemble training using gradient boosted trees includes training an ensemble of trees. The training includes splitting a training dataset into several data portions. Each data portion is assigned to each thread group from a set of thread groups. The training further includes executing a stage, in which each thread group, in parallel, trains a respective ensemble of decision trees. Executing the stage includes performing, by each thread group, in parallel, machine learning operations for the respective ensemble of decision trees using the data portion assigned to each thread group. Further, each thread group validates, in parallel, the respective ensemble of decision trees using a data portion assigned to another thread group. Execution of the stage is repeated until a predetermined threshold is satisfied. Further, a prediction is inferenced using the ensemble of decision trees that is formed using the respective ensemble of trees from each of the thread groups.

Communication-efficient data parallel ensemble boosting

Embodiments for creating compact example subsets for intent classification in a conversational system are provided. A set of content used for training an intent classifier is received from a conversational corpus. Entries within the set of content are separated into a first subset and a second subset, and a cross-validation operation is performed on the first and second subsets to identify a correctly labeled portion and an incorrectly labeled portion of the set of content. A reduced content used for performing a final training of the intent classifier is formed by combining a first number of the entries from the correctly labeled portion and a second number of the entries from the incorrectly labeled portion of the set of content.

Creating compact example sets for intent classification

A combination of propagation operations and learning algorithms is applied, using a selected set of labeled conversational logs retrieved from a subset of a plurality of conversational logs, to a remaining corpus of the plurality of conversational logs to train an automated response system according to an intent associated with each of the conversational logs. The combination of propagation operations and learning algorithms may include defining the labels by a user for the selected set of the subset of the plurality of conversational logs; training a probabilistic classifier using the defined labels of features of the selected set, wherein the probabilistic classifier produces labeling decisions for the subset of conversational logs; weighting the features of the selected set in a model optimization process; and/or training an additional classifier using the weighted features of the selected set and applying the additional classifier to the remaining corpus.

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

A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.

Method, apparatus, and system for conflict detection and resolution for competing intent classifiers in modular conversation system

A method and apparatus are provided for automatically generating and processing first and second concept vector sets extracted, respectively, from a first set of concept sequences and from a second, temporally separated, concept sequences by performing a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect changes in the corpus over time by identifying changes for one or more concepts included in the first and/or second set of concept sequences.

Analyzing concepts over time

A method and apparatus are provided for recommending concepts from a first concept set in response to user selection of a first concept Ci by performing a natural language processing (NLP) analysis comparison of vector representations of user concepts contained in written content authored by the user and candidate concepts in a first concept set to determine a similarity measure for each candidate concept, and to select therefrom one or more of the candidate concepts for display as recommended concepts which are related to the user concepts contained in written content authored by the user based on the similarity measure between each candidate concept and each user concept.

Content authoring

A first observation window in a first time series is identified. The first observation window is preceded by a first portion of the first time series. A neural network is trained using the first portion of the first time series and the first observation window, and weights are extracted from the middle layers of the neural network. A first feature vector is generated based on the weights. A second observation window in a second time series is identified, where the second observation window is preceded by a first portion of the second time series. A second feature vector associated with the second observation window is determined. The second feature vector is based at least in part on the first set of weights. A similarity between the first and second observation windows is determined based on comparing the first feature vector and the second feature vector.

Comparing time series data using context-based similarity

Embodiments provide for driving intent expansion via anomaly detection by ranking, according to anomaly scores, a plurality of historic utterances that have been associated by a classifier with a given intent of a plurality of predefined intents; identifying a given utterance from the plurality of historic utterances having a given anomaly score greater than an anomaly threshold; in response to verifying that the given utterance is associated with the given intent, adding the given utterance to a training dataset as a positive example for the given intent; and in response to verifying that the given utterance is not associated with the given intent, adding the given utterance to the training dataset as a complement example for the given intent. A complement example for one intent may be added as a positive example for a different intent. The training dataset may be used to train or retrain an intent classifier.

Driving intent expansion via anomaly detection in a modular conversational system

A computer-implemented method, cognitive intelligence system and computer program product adapt a relational database containing multiple data types. Non-text tokens in the relational database are converted to a textual form. Text is produced based on relations of tokens in the relational database. A set of word vectors is produced for the tokens based on the text. A cognitive intelligence query expressed as a structured query language (SQL) query may be applied to the relational database using the set of word vectors. The form of non-text tokens is one of a numeric value, an SQL type, an image, a video, a time series, latitude and longitude, or chemical structures. A single word embedding model may be applied over one or more tokens in the text. A plurality of sets of preliminary word vectors are computed by applying more than one embedding model over all tokens in the text. The preliminary word vector sets are merged to form the set of word vectors.

Search queries of multi-datatype databases

From a first attribute-value pair in a record, new data comprising a first token is created. From each token using a processor and a memory, new data including a corresponding vector is computed. From the record, a target row is selected, wherein a target attribute-value pair in the target row includes a value requiring correction. Using a similarity measure, a set of most similar rows to the target row is determined, wherein each row in the set of most similar rows to the target row has a corresponding similarity measure above a threshold similarity measure and wherein each row in the set of most similar rows includes the target attribute. From values corresponding to the target attribute in the set of most similar rows, a replacement value is determined. The value requiring correction in the target row is replaced with the replacement value.

Record correction and completion using data sourced from contextually similar records

Structured and semi-structured databases and files are processed using natural language processing techniques to impute data for null value tokens in database records from other records that have non-null values for the same attributes. Vector embedding techniques are used, including, in some cases, appropriately tagging null value tokens to reduce or eliminate their undue impact on semantic vectors generating using a neural network.

Vector embedding models for relational tables with null or equivalent values

Evaluating intent authoring processes, by a processor in a computing environment. A dataset comprising utterances of interactive dialog sessions between agents and clients for a given product or service is received. A classification of at least a portion of the utterances is performed for a target intent according to at least one of a plurality of recommendation algorithms, where the classification is performed by an automatic driver invoking the recommendation algorithm and simulating a manual confirmation of the algorithm's decision by a user. A classifier trained with the utterances recommended and confirmed by the automatic driver is automatically evaluated according to at least one of the plurality of evaluation criteria. A report tracking the evaluation results is generated.

Evaluation framework for intent authoring processes

Evaluating intent authoring processes, by a processor in a computing environment. Results are received of a simulated intent labeling effort of a dataset comprising utterances of interactive dialog sessions between agents and clients for a given product or service. Figures of merits for respective algorithms used to perform the simulated intent labeling effort are computed. Each of the respective algorithms are evaluated according to the computed figures of merits; and one of the respective algorithms is implemented for labeling intents of a remaining corpus of the synthesized dataset according to parameters evaluated in the computed figures of merits.

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Millburn, NJ, United States of America

Tin Kam Ho