Jio Platforms Limited

Chinesh Doshi

Prasad Pradip Joshi

Naman Gupta

The present invention provides a robust and effective solution for a novel pipeline to fine-tune the embedding with very less data and computational resources for making the embeddings semantically and contextually aware. Automated system and method for hyper parameter tuning and retrofitting formulation of information into the embeddings and then propose novel smooth extension to the formulation with a new way to automatically tune hyper parameters pipeline which can be used for wide business problems.

Automated system and method for hyper parameter tuning and retrofitting formulation

The present disclosure provides a system () for retrofitting words represented using the vectors for Natural Language Processing (NLP) models and a streamlined process which is an ideal pipeline for any NLP tasks. The system () may discover the user meta data or k-nuggets in five stages for retrofitting and stacking the retrofitted embeddings. Further, the system () may use the retrofitted embeddings for NLP Tasks. The five stages of the k-nugget discovery pipeline are Lexical, Syntactic, Semantic, transactional, and language agnostic stages for retrofitting the word embeddings. The proposed embedding layer is replaced with the retrofitted embedding which may be obtained after the fifth stage and improved performance can be achieved. To validate the approach, the K-nugget discovery pipeline has been tested on the SemEval (Hinglish and English Tweet dataset) and HOT dataset (Hinglish Tweet dataset) and to achieve state of the art results on the test dataset.

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Madhya Pradesh, India

Chinesh Doshi