BART: Denoising Sequence-to-Sequence Pre-training for NLG (Research Paper Walkthrough)
May 01, 2021
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#bart #transformers #naturallanguageprocessing
The authors from Facebook AI propose a new pre-training objective for sequence models as denoisining autoencoder. It can be viewed as corrupting text with some arbitrary noise function while the Language Model is expected to denoise it. Watch video to know more :)
⏩ Abstract: We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.
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⏩ OUTLINE:
0:00 - Abstract & Background (De-noising Autoencoder)
04:04 - BERT vs GPT vs BART
06:51 - BART Model
07:41 - Pre-training BART (Token Masking, Token Deletion, Text Infilling, Sentence Permutation, Document Rotation)
⏩ Paper Title: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
⏩ Paper: https://arxiv.org/abs/1910.13461
⏩ Author: Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer
⏩ Organisation: Facebook AI
⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
Full Playlist on Query Expansion for Information Retrieval using NLP: https://www.youtube.com/watch?v=QpTZ_-6uio8&list=PLsAqq9lZFOtXsJ_S_lB9pPz2cbz-i2DD0
Full Playlist on Text Generation Evaluation Techniques: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
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#techviz #datascienceguy #research #autoencoder #denoising
0:00 Abstract & Background (De-noising Autoencoder) 04:04 BERT vs GPT vs BART 06:51 BART Model 07:41 Pre-training BART (Token Masking, Token Deletion, Text Infilling, Sentence Permutation, Document Rotation)
0:00 Abstract & Background (De-noising Autoencoder) 04:04 BERT vs GPT vs BART 06:51 BART Model 07:41 Pre-training BART (Token Masking, Token Deletion, Text Infilling, Sentence Permutation, Document Rotation)
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