International Business Machines Corporation

Parag Jain

Karthik Sankaranarayanan

Abhijit Mishra

Anirban Laha

Amar Prakash Azad

Saravanan Krishnan

Disha Shrivastava

Vijay Ekambaram

Srikanth Govindaraj Tamilselvam

Diptikalyan Saha

Sampath Dechu

Jaydeep Sen

Senthil Kumar Kumarasamy Mani

Pranay Kumar Lohia

Priyanka Agrawal

Ashish Mittal

Saneem Ahmed Chemmengath

Anush Sankaran

Shreya Khare

Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes selecting at least one set of style specifications for transforming at least a portion of input text. The at least one set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text, and weights for each of a set of linguistic aspects for transformation of at least a portion of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network.

Controllable style-based text transformation

Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying amendable portions of input text by processing at least a portion of the input text using at least one neural network; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein said determining comprises processing at least a portion of the set of stylistic parameters using at least one neural network; generating a stylized output set of text by transforming at least a portion of the input text, wherein said transforming comprises modifying at least one of the amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to at least one user.

Unsupervised tunable stylized text transformations

Methods, systems, and computer program products related to a cognitive assistant for co-generating creative content are provided herein. A computer-implemented method includes obtaining semantic-level inputs from at least one user, wherein the semantic-level inputs pertain to multiple aspects of a desired content narrative; generating textual content based at least in part on the semantic-level inputs, wherein said generating the textual content comprises applying one or more deep learning algorithms to the semantic-level inputs; generating image content based at least in part on the generated textual content; creating the desired content narrative by integrating (i) the generated textual content and (ii) the generated image content; and outputting the desired content narrative to the at least one user.

Cognitive assistant for co-generating creative content

Text suggestions are generated. A document is received, and a portion score for at least one portion of the document is generated. A global assessment score for at least two portions of the document is also generated. A variation between the portion score and the global assessment score is calculated. It is determined that the variation is above a threshold variation, and at least one text change suggestion is generated.

Real-time assessment of text consistency

One embodiment provides a method, including: receiving, from a user, input for generating an article, wherein the input comprises (i) an indication of topic-specific requirements for the article that are dependent upon a topic of the article and (ii) an indication of non-topic-specific requirements for the article that are independent of the topic of the article; creating at least one query from the input; retrieving, from secondary sources by performing a search utilizing the query, structured information contextually relevant to the topic; and from the structured information, generating the article, wherein the article comprises a coherent and grammatically fluid structured article generated in view of the topic-specific requirements and the non-topic-specific requirements.

Structured article generation

Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes obtaining input text and selecting a set of style specifications for transforming the input text. The set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of the input text, and weights for each of a set of linguistic aspects for transformation of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the set of style specifications utilizing an unsupervised neural network.

Embodiments of the present invention provide a method, a computer program product, and a system for generating a haptic signal representing a fabric composition. Embodiments of the present invention can be used to generate a haptic signal that is based on a user selection. For example, embodiments of the present invention can combine characteristic signals corresponding to a plurality of textiles to generate the haptic signal for output to a haptic device. Embodiments of the present invention can be used to recommend similar fabric compositions based upon similarity between a characteristic signal of a fabric composition and the haptic signal.

Simulating tactile information for haptic technology

Methods, systems, and computer program products for unsupervised tunable stylized text transformations are provided herein. A computer-implemented method includes identifying stylistically amendable portions of input text by applying at least one neural network to the input text; determining stylistic text modifications to the amendable portions of the input text, the text modifications encompassing a set of stylistic parameters, wherein the determining comprises applying at least one neural network to the set of stylistic parameters; generating a stylized output set of text by transforming the input text, wherein the transforming comprises modifying at least one of the stylistically amendable portions of the input text via at least one of the stylistic text modifications encompassed by the set of stylistic parameters; and outputting the stylized output set of text to a user.

Techniques for script modification are provided including receiving a script and parsing the script to identify at least one attribute of the script. The identified at least one attribute is presented to a user in a graphical user interface via a display device and an adjustment of at least one element in the graphical user interface that corresponds to the at least one attribute is received from the user via an input device. Modification data corresponding to the at least one attribute are received from a data repository and at least one attribute of the script is modified based on the received adjustment and the obtained modification data corresponding to the at least one attribute. A modified script is generated based on the modified at least one attribute.

Script modification

Methods, systems, and computer program products for bootstrapping of state-based dialog systems are provided herein. A computer-implemented method includes determining parameters for state automata by partitioning an ontology graph into sub-graphs and a knowledge graph into sub-graphs, wherein the ontology graph and the knowledge graph are based on a user question and domain knowledge pertaining to the user question; generating a structured query for each of the sub-graphs; determining intentions of a dialog pertaining to the at least one user question by translating each of the generated structured queries to a respective natural language query; creating one or more dialog states for each of the determined dialog intentions; creating one or more connecting dialog states between pairs of the created dialog states; and generating an automata dialog framework associated with the user question based on the created dialog states and the created connecting dialog states.

Ontology-based automatic bootstrapping of state-based dialog systems

Techniques for the generation of variable natural language descriptions from structured data are disclosed including receiving input data and generating a first plurality of tuples based on the received input data. A first plurality of sentences and a corresponding second plurality of tuples are obtained from a data repository based on the first plurality of tuples. A second plurality of sentences is generated based on the second plurality of tuples, the first plurality of sentences, and the first plurality of tuples. A sentence is selected from the first plurality of sentences and the second plurality of sentences for each of the first plurality of tuples. At least one paragraph variation is generated where each paragraph variation is generated based on two or more of the selected sentences. The at least one paragraph variation is presented as a natural language description of the input data.

Generation of variable natural language descriptions from structured data

Aspects of the present disclosure relate to techniques for presenting content on a plurality of devices. The techniques including determining a first configuration for displaying first content on a plurality of devices, where the first configuration is based on a collective viewing size, an aspect ratio, a resolution shift, an internal border area, and a tactile quality gradient. The techniques including displaying, on a first device of the plurality of devices, a first device identifier, an orientation indicator, and an adjacent device identifier.

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Bengaluru, India

Parag Jain