Abstract: Methods, systems, and computer program products for active learning for natural language question answering are provided herein. A computer-implemented method includes generating a semantic signature of a natural language query; generating a SQL signature for a SQL query corresponding to the natural language query; determining whether a set of mappings includes a semantic signature matching the generated semantic signature, wherein each mapping in the set is between (i) a given semantic signature and (ii) a SQL signature representing a class of SQL queries corresponding to the given semantic signature; and in response to determining that the set of mappings does not include the generated semantic signature, adding a mapping between the generated semantic signature and the generated SQL signature to the set of mappings.

Abstract: An embodiment for identifying and processing poly-process natural language queries may include receiving a natural language query. The embodiment may also automatically identify a bridge entity in the received natural language query. The embodiment may also automatically determine whether the received natural language query is a poly-process query. The embodiment may further include, in response to identifying that the received natural language query is the poly-process query, automatically generating sub-queries for each process in the poly-process query and generate results for each sub-query. The embodiment may also automatically combining the results of each sub-query using the bridge entity to output a combined result. The embodiment may further include automatically generating a modified sub-query for post-processing of the combined result. The embodiment may also automatically process the modified sub-query to generate a final query result for the received natural language query.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate multi-lingual query interpretation. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an annotation component that generates one or more language invariant signals, an interpretation component that generates a complete query intent using the one or more language invariant signals, and a translation component that processes the complete query intent to an executable backend query to facilitate multi-lingual query interpretation. In one or more embodiments, the translation component can be operatively connected with the interpretation component to generate a zero-shot transfer of the one or more language invariant signals.

Abstract: A method, a structure, and a computer system for multilingual learning. The exemplary embodiments may include training, for each language in a set of two or more languages, a model for a task and identifying one or more important words appearing in at least two of the models. The exemplary embodiments may further include weighting one or more conflicts and one or more overlaps between the one or more important words, as well as generating a cluster of at least two languages of the set based on an aggregate of the weighting.

Abstract: An embodiment including a domain-agnostic natural language processing system for processing natural language queries having an explainable interpretation feedback model is provided. The embodiment may include receiving a natural language query. The embodiment may also include to automatically detecting whether the received natural language query includes implicit intent therein. The embodiment may include, in response to detecting implicit intent in the received natural language query, automatically generating a modified query including a default inference from an interpretation fact sheet. The embodiment may further include automatically presenting the modified query to the user and asking the user for feedback on the modified query.

Abstract: An embodiment for identifying and processing poly-process natural language queries may include receiving a natural language query. The embodiment may also automatically identify a bridge entity in the received natural language query. The embodiment may also automatically determine whether the received natural language query is a poly-process query. The embodiment may further include, in response to identifying that the received natural language query is the poly-process query, automatically generating sub-queries for each process in the poly-process query and generate results for each sub-query. The embodiment may also automatically combining the results of each sub-query using the bridge entity to output a combined result. The embodiment may further include automatically generating a modified sub-query for post-processing of the combined result. The embodiment may also automatically process the modified sub-query to generate a final query result for the received natural language query.

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: Methods, systems, and computer program products for automated code-mixed natural language processing for artificial intelligence-based question answering techniques are provided herein. A computer-implemented method includes detecting multiple languages in an input query to an artificial intelligence-based question answering system; determining, in the input query, one or more partial query signals associated with each of the multiple languages; identifying one or more missing entity arguments from at least a portion of the one or more partial query signals; updating at least a portion of the one or more missing entity arguments by inferring data from at least a portion of the one or more partial query signals using at least one artificial intelligence technique; and performing one or more automated actions based at least in part on the updating of at least a portion of the one or more missing entity arguments.

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: A query can be received from a user. The query can be sent to a plurality of automated agents to process the query. Results and associated confidence scores can be received from the plurality of automated agents. At least some of the results and associated confidence scores can be probed, based at least on a reason given for a result having the highest associated confidence score among the received results and associated confidence scores, to select an automated agent from the plurality of automated agents for answering the query. Information can be stored, where the information can include at least the results and associated confidence scores and a selected automated agent for answering the query, where at least one of the plurality of automated agents learns from the stored information to update its confidence score in answering the query.

Abstract: Methods, systems, and computer program products for processing natural language analytics queries are provided herein.

Abstract: Methods, systems, and computer program products for active learning for natural language question answering are provided herein. A computer-implemented method includes generating a semantic signature of a natural language query; generating a SQL signature for a SQL query corresponding to the natural language query; determining whether a set of mappings includes a semantic signature matching the generated semantic signature, wherein each mapping in the set is between (i) a given semantic signature and (ii) a SQL signature representing a class of SQL queries corresponding to the given semantic signature; and in response to determining that the set of mappings does not include the generated semantic signature, adding a mapping between the generated semantic signature and the generated SQL signature to the set of mappings.

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 a query from a user; identifying that a desired definition of the at least one term is unknown, by determining that the at least one term does not map to a term having a known definition; receiving the definition of the at least one term from the user; adding the definition to a domain grammar comprising (i) domain-specific terminology and (ii) definitions corresponding to the terms within the domain grammar, wherein the adding comprises (a) extracting expressions from the requested definition and (b) adding, for the at least one term, the expressions into a structured format within the domain grammar; combining (iii) the requested definition and (iv) terms from the parsed query having previously known definitions into a complete query; and providing a response to the query by executing the complete query on a knowledge store.

Abstract: A natural language processing approach for generating domain specific reasoning-based meaning representations. The approach may include receiving a user query via structured or unstructured data. The approach may also include generating a structured query from the user query using domain specific ontology and universal facts. Further, the approach many include the structured query may be analyzed to determine if the structured query has been assigned consistent concepts, properties and actions. Additionally, the approach may involve correcting the structured query if it is determined the structured query is inconsistent with the domain ontology and the universal facts.

Abstract: Embodiments are disclosed for correcting a natural language interface database (NLIDB) system. The techniques include receiving feedback indicating that an answer provided in response to a question for an NLIDB system is inaccurate. The techniques further include finding an ontology element for a datastore of the NLIDB system that matches to the feedback. The techniques also include selecting candidate annotations for the NLIDB system based on the ontology element and a data type of the ontology element. Additionally, the techniques include generating a question-answer (QA) pair for each of the candidate annotations. Further, the techniques include adding one of the candidate annotations to annotations for a natural language query (NLQ) engine of the NLIDB system based on a client verification of the QA pair.

Abstract: One embodiment provides a method, including: receiving a query from a user; identifying that a desired definition of the at least one term is unknown, by determining that the at least one term does not map to a term having a known definition; receiving the definition of the at least one term from the user; adding the definition to a domain grammar comprising (i) domain-specific terminology and (ii) definitions corresponding to the terms within the domain grammar, wherein the adding comprises (a) extracting expressions from the requested definition and (b) adding, for the at least one term, the expressions into a structured format within the domain grammar; combining (iii) the requested definition and (iv) terms from the parsed query having previously known definitions into a complete query; and providing a response to the query by executing the complete query on a knowledge store.

Abstract: Systems and methods are provided for generating nested queries from natural language queries. In particular, system and methods are provided to implement natural language interfaces to databases (NLIDB) frameworks which are configured to apply intelligent reasoning over domain semantics to detect and generate nested queries across different domains without the need for domain specific training or utilizing domain-specific semantic templates for mapping a natural language query to a structured query.