Abstract: Techniques for enhanced table and text question answering based on multi-instance, multi-answer training are presented. An answer extractor component can determine answer scores associated with candidate answer data items based on analysis of a set of data, comprising row data items of a table and passage data items associated with the table, and a context of a query of the set of data. The answer extractor component can be trained based on application of denoised single-instance and multiple-instance answer matching data associated with contexts to an answer extractor model to generate a trained answer extractor model of the answer extractor component. A query response component can determine a correct answer data item responsive to the query from the candidate answer data items based on the answer scores associated with the candidate answer data items, wherein the candidate answer data items can be reranked based on reweighted answer scores.

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 or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to building a unified question generation system across languages and modalities. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a training component that can train a unified question generation model to generate questions in a language from a first modality in the language using training data comprising one or more second modalities in the language different from the first modality, wherein the first modality and the one or more second modalities can include at least one of one or more tables, one or more passages, or a combination of the one or more tables and the one or more passages.

Abstract: Mechanisms are provided for processing a sequential database natural language query. A process model is preprocessed to generate mapping data structure(s). The mapping data structure(s) map elements of the sequential process to other elements of the sequential process to thereby identify sequential and dependent characteristics of the sequential process. A sequential database natural language (SDNL) query interpretation engine is configured with the mapping data structure(s) and natural language processing is performed on a query to generate extracted features. The configured SDNL query interpretation engine operates on the extracted features and the mapping data structure(s) to generate intent information for the query. Executable database queries are generated based on the intent and executed on a database to return a response to the query.

Abstract: Retrieval aware natural language question generation for open domain document retrieval is provided. In one aspect, a system for retrieval aware question generation includes: a question decontextualizer configured to decontextualize a question generated from a context of a target document by adding terms from the context into the question itself to create a decontextualized question, where the decontextualized question alone enables open domain document retrieval without a need for also providing the context. The system can also include a detect document identifier configured to find the terms in the context; and a retriever configured to retrieve documents from the corpus of documents using the decontextualized question. A method for retrieval aware question generation using the present system is also provided.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate a Question Generation System. 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 a receiving component that receives a corpus of documents that contain Tables (Ts) and Passages (Ps) for performing natural language processing (NLP); an executing component that executes the NLP by employing the tables (sT) and passages (Ps) as primary inputs; and a query component that generates an output Question (Q) based on a subset of the tables Ts and passages (Ps).

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: Techniques for enhanced table and text question answering based on multi-instance, multi-answer training are presented. An answer extractor component can determine answer scores associated with candidate answer data items based on analysis of a set of data, comprising row data items of a table and passage data items associated with the table, and a context of a query of the set of data. The answer extractor component can be trained based on application of denoised single-instance and multiple-instance answer matching data associated with contexts to an answer extractor model to generate a trained answer extractor model of the answer extractor component. A query response component can determine a correct answer data item responsive to the query from the candidate answer data items based on the answer scores associated with the candidate answer data items, wherein the candidate answer data items can be reranked based on reweighted answer scores.

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.