BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Proceedings of NAACL-HLT 2019, pages 4171–4186 Minneapolis, Minnesota, June 2 - June 7, 2019. c⃝2019 Association for Computational Linguistics 4171 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova Google AI Language {jacobdevlin,mingweichang,kentonl,kristout}@google.com Abstract We introduce a new language representa- tion model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language repre- sentation models (Peters et al., 2018a; Rad- ford 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 re- sult, 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 re- sults 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 answer- ing Test F1 to 93.2 (1.5 point absolute im- provement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). 1 Introduction Language model pre-training has been shown to be effective for improving many natural language processing tasks (Dai and Le, 2015; Peters et al., 2018a; Radford et al., 2018; Howard and Ruder, 2018). These include sentence-level tasks such as natural language inference (Bowman et al., 2015; Williams et al., 2018) and paraphrasing (Dolan and Brockett, 2005), which aim to predict the re- lationships between sentences by analyzing them holistically, as well as token-level tasks such as named entity recognition and question answering, where models are required to produce fine-grained output at the token level (Tjong Kim Sang and De Meulder, 2003; Rajpurkar et al., 2016). There are two existing strategies for apply- ing pre-trained language representations to down- stream tasks: feature-based and fine-tuning. The feature-based approach, such as ELMo (Peters et al., 2018a), uses task-specific architectures that include the pre-trained representations as addi- tional features. The fine-tuning approach, such as the Generative Pre-trained Transformer (OpenAI GPT) (Radford et al., 2018), introduces minimal task-specific parameters, and is trained on the downstream tasks by simply fine-tuning all pre- trained parameters. The two approaches share the same objective function during pre-training, where they use unidirectional language models to learn general language representations. We argue that current techniques restrict the power of the pre-trained representations, espe- cially for the fine-tuning approaches. The ma- jor limitation is that standard language models are unidirectional, and this limits the choice of archi- tectures that can be used during pre-training. For example, in OpenAI GPT, the authors use a left-to- right architecture, where every token can only at- tend to previous tokens in the self-attention layers of the Transformer (Vaswani et al., 2017). Such re- strictions are sub-optimal for sentence-level tasks, and could be very harmful when applying fine- tuning based approaches to token-level tasks …

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