International Business Machines Corporation

Mo Chi Liu

Guang Ming Zhang

Hong Wei Jia

Yun Bj Wang

Xiaoyang Yang

Wu Yan

Yun Ting Wang

Chu Yun Tong

He Yang

Embodiments of the present disclosure relate to a solution for translation verification and correction. According to the solution, a neural network is trained to determine an association degree among a group of words in a source or target language. The neural network can be used for translation verification and correction. According to the solution, a group of words in a source language and translations of the group of words in a target language are obtained. An association degree among the group of words and an association degree among the translations can be determined by using the trained neural network. Then, whether there is a wrong translation can be determined based on the association degrees. In some embodiments, corresponding methods, systems and computer program products are provided.

Translation verification and correction

A method of operating a shared computing environment includes receiving a client request for an optimized runtime configuration of the shared computing environment for executing a task, receiving runtime data of the task collected from a prior runtime, inferring a runtime configuration from the runtime data, determining a predicted timeseries workload for the shared computing environment, updating the runtime configuration to including the timeseries workload prediction, wherein the updating outputs the optimized runtime configuration, and configuring the shared computing environment according to the optimized runtime configuration in executing the task.

Machine learning based runtime optimization

Systems and methods for computer-automated labeling of data are disclosed. In embodiments, a method includes: identifying technical data assets in lineage data and corresponding business items in User Interface (UI) data of a user, wherein the lineage data includes a data source for the UI data; mapping the technical data assets to the corresponding business items; determining relevant labels to assign to the technical data assets from a label repository based on a similarity analysis of names of the corresponding business items and labels in the label repository; determining that one or more of the relevant labels meet a confidence threshold based on the similarity analysis; and automatically assigning the one or more of the relevant labels to associated ones of the technical data assets based on the determining that the one or more of the relevant labels meet the confidence threshold.

Automated data labeling

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Beijing, China

Mo Chi Liu