International Business Machines Corporation

Tarun Tater

Jaydeep Sen

Vijay Ekambaram

Srikanth Govindaraj Tamilselvam

Namit Kabra

Vatche Isahagian

Anush Sankaran

Naveen Panwar

Vinod Muthusamy

Saranga Dhar Tripathy

Sampath Dechu

Gargi Banerjee Dasgupta

Senthil Kumar Kumarasamy Mani

Monika Gupta

Yara Rizk

Anvita Vyas

Yazan Obeidi

Tanmayee Narendra

Jassimran Kaur

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.

Adaptive answer confidence scoring by agents in multi-agent system

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.

Multi-lingual natural language query

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.

Identifying and processing poly-process natural language queries

An approach is disclosed that receives a code segment from a source code file. The received code segment is inputted to a trained knowledge manager from which one or more comparable code segments are received. Each of the comparable code segments are identified as having similar functionality as the selected code segment and each of the comparable code segments has a set of metadata. The metadata is used to select one of the comparable code segments and this selected code segment is used to replace the original code segment in the source code file.

Identify, suggest, and auto-implement best practices for requirement specific software development

Methods, systems, and computer program products for combined data pre-process and architecture search for deep learning models are provided herein. A computer-implemented method includes obtaining data corresponding to a deep learning task; performing, based on the deep learning task and the data, a multi-objective learning process to select an optimal combination of (i) a deep learning architecture for the deep learning task and (ii) a data pre-processing strategy to be applied to the data, the data pre-processing strategy comprising one or more pre-processing steps; pre-processing the data for the selected deep learning architecture based on the data pre-processing strategy; and providing the pre-processed data as input to the selected deep learning architecture to perform the deep learning task.

Combined data pre-process and architecture search for deep learning models

Techniques for build recovery from inter-code commit failure. A build error for a software project is identified. A first software module, with one or more errors related to the build error, is selected. A comparison software module for the first software module is identified. The comparison software module includes at least one of: (i) a sibling software module to the first software module or (ii) an earlier version of the first software module. A potential problem with the first software module, related to the build error, is determined based on comparing the first software module with the comparison software module. A solution to the potential problem is generated based on the first software module. The solution includes a modification to the software code of the first software module. The solution is applied by modifying the software code of the first software module.

Cognitive build recovery from inter-code commit issues

Embodiments describe an approach for automatically generating feedback for an online forum. Embodiments determine if a user is using a solution to a problem, wherein the solution is posted on an online forum, and responsive to determining the user is using the solution, capturing environment information associated with the user's computing device. Additionally, embodiments determine if the solution solved the problem, and responsive to determining the solution solved the problem, automatically generate feedback associated with the solution, wherein the feedback comprises the environment information and information detailing that the solution solved the problem.

Online forum automated feedback generator

One embodiment provides a method, comprising: identifying, using a processor, an individual in a video segment; ascertaining, from at least the video segment, a viewpoint expressed by the individual on a topic, wherein the viewpoint comprises at least one of: a stance of the individual and a sentiment of the individual toward the topic; identifying, using a processor, a superset video comprising the video segment, wherein the superset video is an originally published video; ascertaining, based at least in part on the superset video, an overarching viewpoint of the individual on the topic; determining whether an inconsistency exists between the viewpoint expressed by the individual in the video segment and the overarching viewpoint of the individual ascertained in the superset video; and alerting, responsive to determining that an inconsistency exists, a user that the video segment contains the inconsistency.

Out-of-context video detection

Aspects of the present invention disclose a method for debiasing text by neutralizing subjective information while preserving relevant information. The method includes one or more processors determining a sentiment of textual data. The method further includes extracting information that is relevant to resolving a topic that is represented in the textual data. The method further includes debiasing the information that is relevant to resolving a topic that is represented in the textual data. The method further includes validating that the debiased information includes the information that is relevant to resolving the topic that is represented in the textual data. The method further includes generating debiased textual data, the debiased textual data including the debiased information and masking the determined sentiment of the textual data.

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Bengaluru, India

Tarun Tater