Proceedings ArticleDOI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin,Ming-Wei Chang,Kenton Lee,Kristina Toutanova +3 more
- pp 4171-4186
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TLDR
BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.Abstract:
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).read more
Citations
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Proceedings ArticleDOI
Fine-grained Sentiment Classification using BERT
TL;DR: A promising deep learning model called BERT is used to solve the fine-grained sentiment classification task and it is shown that this model outperforms other popular models for this task without sophisticated architecture.
Proceedings ArticleDOI
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval
TL;DR: This paper is able to leverage passage-level relevance judgments fortuitously available in other domains to fine-tune BERT models that are able to capture cross-domain notions of relevance, and can be directly used for ranking news articles.
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Reformulating Unsupervised Style Transfer as Paraphrase Generation.
TL;DR: This paper reformulates unsupervised style transfer as a paraphrase generation problem, and presents a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data that significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations.
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Improved Conditional VRNNs for Video Prediction
TL;DR: In this paper, a hierarchy of latent variables is proposed to increase the expressiveness of the latent distributions and to use higher capacity likelihood models to better model the probability of future sequences.
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DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
TL;DR: A novel pre-trained bidirectional encoder representation that forms global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts, named DNABERT, and can be readily applied to other organisms with exceptional performance.
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