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
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
TL;DR: The authors proposed a multi-passage BERT model to globally normalize answer scores across all passages of the same question, and this change enables our QA model find better answers by utilizing more passages.
Proceedings ArticleDOI
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation
TL;DR: This work proposes to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained, and distill the neural architecture (DNA) knowledge from a teacher model to supervise the block-wise architecture search, which significantly improves the effectiveness of NAS.
Proceedings ArticleDOI
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks
TL;DR: It is found that doing fine-tuning on multiple languages together can bring further improvement in Unicoder, a universal language encoder that is insensitive to different languages.
Posted Content
FlauBERT: Unsupervised Language Model Pre-training for French
Hang Le,Loïc Vial,Jibril Frej,Vincent Segonne,Maximin Coavoux,Benjamin Lecouteux,Alexandre Allauzen,Benoît Crabbé,Laurent Besacier,Didier Schwab +9 more
TL;DR: This paper introduces and shares FlauBERT, a model learned on a very large and heterogeneous French corpus and applies it to diverse NLP tasks and shows that most of the time they outperform other pre-training approaches.
Proceedings ArticleDOI
Probing Pretrained Language Models for Lexical Semantics
TL;DR: A systematic empirical analysis across six typologically diverse languages and five different lexical tasks indicates patterns and best practices that hold universally, but also point to prominent variations across languages and tasks.
References
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Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings ArticleDOI
Deep contextualized word representations
Matthew E. Peters,Mark Neumann,Mohit Iyyer,Matt Gardner,Christopher Clark,Kenton Lee,Luke Zettlemoyer +6 more
TL;DR: This paper introduced a new type of deep contextualized word representation that models both complex characteristics of word use (e.g., syntax and semantics), and how these uses vary across linguistic contexts (i.e., to model polysemy).