International Business Machines Corporation

Amazon Technologies, Inc.

Rensselaer Polytechnic Institute

Li Zhang

Wei Zhang

Ming Tan

Yandong Wang

Yufei Ren

Shiyu Chang

Mo Yu

Hui Su

Yue Zhao

Shuai Zheng

Zheng Zhang

Xiangyang Mou

Sanjiv Ranjan Das

Sheng Zha

Shenghua Yue

Minwei Feng

Zhijiang He

Xin Huang

A machine receives a first set of global parameters from a global parameter server. Multiple learner processors in the machine execute an algorithm that models an entity type using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.

Parameter data sharing for multi-learner training of machine learning applications

Methods and apparatus for complementary evidence identification in natural language inference. A given question is obtained and a set of N passages is obtained from a database. A probability is determined, for each passage of the set of N passages, of a corresponding passage being a supportive passage for the given question and the set of N passages is ranked based on the determined probabilities. M passages that are ranked 1 to M of the set of N passages are selected. A set of L passages is selected based on a plurality of scores, each score assigned to a set of candidate passages of the set of N passages, each score being based on the determined probabilities, the selected M passages, and a weighted regulation parameter. The set of L passages is provided to a computerized machine learning system to answer the question based on the set of L passages.

Complementary evidence identification in natural language inference

Techniques for machine learning (ML) and natural language processing (NLP) are described. One technique enables the creation of a clean training dataset through just a few API calls. Another technique provides an automated process for generating a domain-specific lexicon, which is then used to generate ML training datasets, in a manner that requires little to no human labor. Another technique gathers ML training data from domain-specific public sources, which are more likely than typical public sources to contain focused terminology and to be free from errors, thus resulting in trained ML models that provide more accurate inferences.

Domain-specific language models

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Li Zhang