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Training Question Answering Models From Synthetic Data (Research Paper Walkthrough)

#nlp #questionanswering #dataaugmentation Can a Q/A model trained on just the Synthetic Data beat SOTA? This research focuses on improving question answering models by generating synthetic questions and answers when dealing with the limited amount of human-annotated data. Author’s achieve better performance on SQUAD1.1 question answering task solely with synthetic data compared to human-annotated questions from the training set. Watch to know more :-) ⏩ Abstract: Question and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains between synthetic and human-generated question-answer pairs. This work aims to narrow this gap by taking advantage of large language models and explores several factors such as model size, quality of pretrained models, scale of data synthesized, and algorithmic choices. On the SQuAD1.1 question answering task, we achieve higher accuracy using solely synthetic questions and answers than when using the SQuAD1.1 training set questions alone. Removing access to real Wikipedia data, we synthesize questions and answers from a synthetic corpus generated by an 8.3 billion parameter GPT-2 model. With no access to human supervision and only access to other models, we are able to train state of the art question answering networks on entirely model-generated data that achieve 88.4 Exact Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. We further apply our methodology to SQuAD2.0 and show a 2.8 absolute gain on EM score compared to prior work using synthetic data. Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe -    / @techvizthedatascienceguy   ⏩ OUTLINE: 0:00 - Abstract & Background 02:02 - Proposed Method (3-step pipeline) 03:31 - Algorithm (Pipeline for generating and evaluating synthetic data) 05:05 - Answer Generation - Equations 06:03 - DataFlow 07:30 - Question Generation 09:05 - Roundtrip filtration 09:37 - Wrap-up ⏩ Paper Title: Training Question Answering Models From Synthetic Data ⏩ Paper: https://arxiv.org/pdf/2002.09599.pdf ⏩ Author: Raul Puri, Ryan Spring, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro ⏩ Organisation: Nvidia, Rice University Blog: https://towardsdatascience.com/traini... Research Paper Summary Playlist:    • Simple Unsupervised Keyphrase Extraction u...   BERT in NLP Playlist:    • LSBert: A Simple Framework for Lexical Sim...   NLP Data Augmentation Playlist:    • Data Augmentation using Pre-trained Transf...   Enjoy reading articles? then consider subscribing to Medium membership, it just 5$ a month for unlimited access to all free/paid content. Subscribe now -   / membership   ********************************************* If you want to support me financially which totally optional and voluntary :) ❤️ You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizC... ********************************************* ⏩ Youtube -    / techvizthedatascienceguy   ⏩ Blog - https://prakhartechviz.blogspot.com ⏩ LinkedIn -   / prakhar21   ⏩ Medium -   / prakhar.mishra   ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter -   / rattller   ********************************************* Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #research #qa #arxiv