User:Yunqian Bao/sandbox

= BART = BART is a model proposed in 2019 that uses a seq2seq architecture with a bidirectional encoder and a left-to-right decoder, achieving state-of-the-art results on various text generation and comprehension tasks, as well as being a pre-training approach that combines Bidirectional and Auto-Regressive Transformers, achieving similar performance to RoBERTa on discriminative tasks and new state-of-the-art results on text generation tasks, and also being a denoising autoencoder used for pretraining sequence-to-sequence models, utilizing a Transformer-based neural machine translation architecture and achieving state-of-the-art results on various tasks such as text generation, comprehension, and machine translation.

History
BART is a pre-training model that combines Bidirectional and Auto-Regressive Transformers. It was proposed in a paper titled "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer on October 29, 2019. The model, known as BART, is a denoising autoencoder designed for pretraining sequence-to-sequence models.

Architecture
BART is a model that utilizes a standard seq2seq/machine translation architecture with a bidirectional encoder (similar to BERT) and a left-to-right decoder (similar to GPT). It can be seen as generalizing BERT and GPT, as it uses a standard Transformer-based neural machine translation architecture. The architecture of BART consists of a sequence-to-sequence model with a bidirectional encoder over corrupted text and a left-to-right autoregressive decoder. Some modifications are made to the standard Transformer architecture, such as using GeLUs as activation functions and initializing parameters from N(0, 0.02). BART contains roughly 10% more parameters than an equivalently sized BERT model. It is trained by corrupting text with an arbitrary noising function and learning a model to reconstruct the original text. Overall, BART is a Transformer-based neural machine translation architecture that incorporates bidirectional encoding and autoregressive decoding, making it a versatile and powerful model for various natural language processing tasks.

Document Corruption Techniques
BART: Training - Document Corruption Techniques

BART: Training - Document Corruption Techniques is a method that allows for various types of document corruption, including token masking, token deletion, text infilling, sentence permutation, and document rotation. This technique has been evaluated using different noising approaches, and it has been found that randomly shuffling the order of the original sentences and utilizing a novel in-filling scheme yield the best performance.

BART for NLP tasks
BART is a powerful pre-training model that has shown comparable performance to other models on discriminative tasks and achieved new state-of-the-art results on text generation tasks, including summarization, dialogue response generation, and abstractive question answering. It has also been found to enhance machine translation decoders by utilizing the entire BART model as a single pretrained decoder. In summary, BART is a versatile pre-training model that can be effectively applied to a wide range of NLP tasks, delivering state-of-the-art results in text generation and comparable performance to other models in discriminative tasks.

Pretraining tasks for encoder
The pretraining tasks for Bart involve shuffling the order of the original sentences and using an in-filling scheme where spans of text are replaced with a single mask token. The pretraining tasks for the encoder include various transformations such as masking random tokens, deleting random tokens, masking a span of tokens with a single mask token (including the option of inserting a mask token), permuting sentences, and rotating the document to start at a specific token.

BART training methods improvement
BART is trained by corrupting documents and optimizing a reconstruction loss. Future work can explore new methods for corrupting documents for pre-training and further improve BART's performance.

BART's training involves corrupting documents and optimizing a reconstruction loss, as described in the paper. In order to enhance BART's performance, future research can focus on developing novel techniques for corrupting documents during pre-training.

Qualitative analysis of BART
Qualitative analysis shows that BART generates fluent and grammatical English output, with high levels of abstraction and integration of supporting evidence.

natural language understanding and generation
BART: Training - natural language understanding and generation is a model that showcases a powerful combination of natural language understanding and generation capabilities. It has been designed to excel in both tasks, demonstrating its versatility and effectiveness in processing and generating human-like text. This model has been extensively trained and fine-tuned to understand and generate natural language, making it a valuable tool in various applications and domains.

Application to downstream tasks
BART is particularly effective for text generation and comprehension tasks. It achieves similar performance to RoBERTa on GLUE and SQuAD with comparable training resources and has achieved state-of-the-art results on various abstractive dialogue, question answering, and summarization tasks, with improvements of up to 6 ROUGE. BART can be fine-tuned for different downstream applications, such as sequence classification tasks, token classification tasks, sequence generation tasks, and machine translation. Additionally, BART outperforms a back-translation system for machine translation, with only target language pretraining.

Properties
BART: Properties

BART is a sequence-to-sequence model with an encoder and a decoder. The encoder is fed a corrupted version of the tokens, while the decoder is fed the original tokens with a mask to hide future words, similar to a regular transformers decoder. This model has the advantage of flexible noising, allowing arbitrary transformations to be applied to the original text. It is recommended to pad the inputs on the right rather than the left when using BART due to its absolute position embeddings.

BART has been shown to be effective for text generation and comprehension tasks. It achieves comparable performance to RoBERTa on GLUE and SQuAD, and it has achieved new state-of-the-art results on abstractive dialogue, question answering, and summarization tasks. Ablation experiments were conducted within the BART framework to measure the factors that most influence end-task performance.

References:

Others
BART is a model that was contributed by sshleifer, and the code from the authors can be found here. It can be seen as a generalization of BERT, GPT, and many other pretraining schemes.