Abstract: One embodiment provides a method, including: receiving a query from a user; providing the query to data owners, wherein each of the data owners has a local machine learning model and wherein the plurality of data owners train a meta-model; secret sharing model output from the data owners between the other data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model; receiving, from each of the plurality of data owners, a set of meta-features corresponding to the query; and generating a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.

Abstract: A method, system, and computer program product for automated artificial intelligence (AI) factsheet generation for modeling and model customization in speech to text (STT) services. The method receives audio data for a user. The audio data contains human speech. Text data is generated, using a first speech to text model, to represent the human speech of the audio data. A set of transcription errors of the first speech to text model are identified. A set of AI factsheets are generated to describe model metadata for the first speech to text model. Based on the set of transcription errors and the set of AI factsheets, the method generates a second speech to text model customized to the user.

Abstract: A method, computer program product and system are provided to generate perturbed text is provided. A processor receives a string of text from a user. A processor determines one or more classifications for at least one word in the string of text by a classification model. A processor determines a plurality of perturbations of the at least one word based on the one or more classifications, where the plurality of perturbations do not share the same one or more classifications as the least one word in the string of text. A processor selects a perturbation of the string of text based on (i) an edit distance between the string of text and the plurality of perturbations, and (ii) a fluency metric for each of the plurality of perturbations. A processor provides the perturbation of the string of text to the user.

Abstract: A question answering bot that digests non-relational data tables is provided. A processor receives a question regarding a non-relational data table. A processor extracts at least one feature of the question using a natural language processing (NLP) model. A processor extracts at least one similar feature of the non-relational data table to the extracted at least one feature of the question. A processor determines at least one relevant cell in the non-relational data table based on the at least one feature of the question and the at least one similar feature of the non-relational data table. A processor provides an answer to the question, where the answer is based on the at least one relevant cell.

Abstract: One embodiment provides a method, including: receiving an indication of an addition of a new participant in a textual communication between at least two existing participants; identifying at least one confidential topic contained within the textual communication by (i) parsing the textual communication and (ii) identifying at least one topic contained within the textual communication; the identifying comprising (i) accessing a confidentiality graph comprising (a) nodes representing participants and (b) edges representing confidential concepts that are acceptable discussion topics between participants connected by a corresponding edge and (ii) determining that an edge corresponding to the at least one confidential topic does not connect the new participant with both of the existing participants; and alerting one of the existing participants that the at least one confidential topic is included in the textual communication to be sent to the new participant.

Abstract: Methods, systems, and computer program products for automated few-shot learning techniques for artificial intelligence-based query answering systems are provided herein. A computer-implemented method includes obtaining multiple sets of queries and answers associated with one or more tables; determining a level of complexity attributed to at least a portion of the queries from the multiple sets of queries and answers; determining, based at least in part on the determined level of complexity attributed to the at least a portion of the queries, one or more new queries for use in training at least one artificial intelligence-based query answering system; facilitating annotation of the one or more new queries; training the at least one artificial intelligence-based query answering system using at least a portion of the one or more annotated new queries; and performing at least one automated action using the at least one trained artificial intelligence-based query answering system.

Abstract: Methods, systems, and computer program products for providing a contextual dialogue framework over dynamic tables are provided herein. A computer-implemented method includes maintaining a context space for a natural language conversation of a user, wherein the context space comprises a dynamic set of one or more tables used for processing at least one query of the natural language conversation; obtaining an additional table associated with an additional query of the natural language conversation; discovering one or more implicit links between the additional table and the dynamic set of tables; updating the context space with the one or more implicit links; and answering the additional query based at least in part on the updated context space.

Abstract: Methods, systems, and computer program products for automatically generating factsheets for artificial intelligence-based question answering systems are provided herein.

Abstract: One embodiment provides a method, including: receiving a query from a user; providing the query to data owners, wherein each of the data owners has a local machine learning model and wherein the plurality of data owners train a meta-model; secret sharing model output from the data owners between the other data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model; receiving, from each of the plurality of data owners, a set of meta-features corresponding to the query; and generating a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.

Abstract: Methods, systems, and computer program products for self-supervision in table question answering are provided herein. A computer-implemented method includes obtaining a table comprising a plurality of entries, wherein each entry corresponds to a particular column and particular row of the table; identifying one or more of the entries in the table that correspond to a target answer of a natural language query; generating an intermediate representation of the table comprising the rows corresponding to the identified one or more entries, wherein the intermediate representation masks each of the identified one or more entries; and generating a set of natural language question and answer pairs based on the intermediate representation.

Abstract: One embodiment provides a method, including: receiving an indication of an addition of a new participant in a textual communication between at least two existing participants; identifying at least one confidential topic contained within the textual communication by (i) parsing the textual communication and (ii) identifying at least one topic contained within the textual communication; the identifying comprising (i) accessing a confidentiality graph comprising (a) nodes representing participants and (b) edges representing confidential concepts that are acceptable discussion topics between participants connected by a corresponding edge and (ii) determining that an edge corresponding to the at least one confidential topic does not connect the new participant with both of the existing participants; and alerting one of the existing participants that the at least one confidential topic is included in the textual communication to be sent to the new participant.