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
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Big Bird: Transformers for Longer Sequences
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TL;DR: It is shown that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model.
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On the Variance of the Adaptive Learning Rate and Beyond
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Journal ArticleDOI
Shortcut learning in deep neural networks
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DeBERTa: Decoding-enhanced BERT with Disentangled Attention
TL;DR: A new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) is proposed that improves the BERT and RoBERTa models using two novel techniques that significantly improve the efficiency of model pre-training and performance of downstream tasks.
Proceedings ArticleDOI
A Structural Probe for Finding Syntax in Word Representations
TL;DR: A structural probe is proposed, which evaluates whether syntax trees are embedded in a linear transformation of a neural network’s word representation space, and shows that such transformations exist for both ELMo and BERT but not in baselines, providing evidence that entire syntax Trees are embedded implicitly in deep models’ vector geometry.
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