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.

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⏩ 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/training-question-answering-models-from-synthetic-data-research-paper-summary-2220186703f
Research Paper Summary Playlist: https://www.youtube.com/watch?v=ykClwtoLER8&list=PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc
BERT in NLP Playlist: https://www.youtube.com/watch?v=uhnKsGDyhEg&list=PLsAqq9lZFOtX-WN8lldIOI7p-p0lBzjtY
NLP Data Augmentation Playlist: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ

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