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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Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT.
Shijie Wu,Mark Dredze +1 more
TL;DR: This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing.
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TL;DR: Comprehensive experiments on both aligned and non-aligned multimodal time-series show that the MulT model outperforms state-of-the-art methods by a large margin, and empirical analysis suggests that correlated crossmodal signals are able to be captured by the proposed cross modal attention mechanism in MulT.
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HellaSwag: Can a Machine Really Finish Your Sentence?.
TL;DR: HellaSwag as discussed by the authors ) is a commonsense NLP dataset where a series of discriminators iteratively select an adversarial set of machine-generated wrong answers, and the key insight is to scale up the length and complexity of the dataset examples towards a critical 'Goldilocks' zone where generated text is ridiculous to humans, yet often misclassified by state-of-the-art models.
Journal ArticleDOI
PCT: Point cloud transformer
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Contrastive Representation Learning: A Framework and Review
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