question: What is the name of the new language representation model introduced in the document?

answer: The document mentions multiple language representation models, so it is unclear which one is being referred to as "new". Can you provide more specific information or context?

context: statistical language modeling. arXiv preprint arXiv:1312.3005 . Z. Chen, H. Zhang, X. Zhang, and L. Zhao. 2018. Quora question pairs. Christopher Clark and Matt Gardner. 2018. Simple and effective multi-paragraph reading comprehen- sion. In ACL.Kevin Clark, Minh-Thang Luong, Christopher D Man- ning, and Quoc Le. 2018. Semi-supervised se- quence modeling with cross-view training. In Pro- ceedings of the 2018 Conference on Empirical Meth- ods in Natural Language Processing , pages 1914– 1925. Ronan Collobert and Jason Weston. 2008. A uniﬁed architecture for natural language processing: Deep neural networks with multitask learning. In Pro- ceedings of the 25th international conference on Machine learning , pages 160–167. ACM. Alexis Conneau, Douwe Kiela, Holger Schwenk, Lo ¨ıc Barrault, and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 Conference on Empirical Methods in Nat- ural Language Processing , pages 670–680, Copen- hagen, Denmark. Association for Computational Linguistics. Andrew M Dai and Quoc V Le. 2015. Semi-supervised sequence learning. In Advances in neural informa- tion processing systems , pages 3079–3087. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei- Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09 . William B Dolan and Chris Brockett. 2005. Automati- cally constructing a corpus of sentential paraphrases. InProceedings of the Third International Workshop on Paraphrasing (IWP2005) . William Fedus, Ian Goodfellow, and Andrew M Dai. 2018. Maskgan: Better text generation via ﬁlling in the.arXiv preprint arXiv:1801.07736 . Dan Hendrycks and Kevin Gimpel. 2016. Bridging nonlinearities and stochastic regularizers with gaus- sian error linear units. CoRR , abs/1606.08415. Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016. Learning distributed representations of sentences from unlabelled data. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computa- tional Linguistics. Jeremy Howard and Sebastian Ruder. 2018. Universal language model ﬁne-tuning for text classiﬁcation. In ACL. Association for Computational Linguistics. Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, and Ming Zhou. 2018. Reinforced mnemonic reader for machine reading comprehen- sion. In IJCAI . Yacine Jernite, Samuel R. Bowman, and David Son- tag. 2017. Discourse-based objectives for fast un- supervised sentence representation learning. CoRR , abs/1705.00557.Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehen- sion. In ACL. Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Skip-thought vectors. In Advances in neural information processing systems , pages 3294–3302. Quoc Le and Tomas Mikolov. 2014. Distributed rep- resentations of sentences and documents. In Inter- national Conference on Machine Learning , pages 1188–1196. Hector J Levesque, Ernest Davis, and Leora Morgen- stern. 2011. The winograd schema challenge. In Aaai spring symposium: Logical formalizations of commonsense reasoning , volume 46, page 47. Lajanugen Logeswaran and Honglak Lee. 2018. An efﬁcient framework for learning sentence represen- tations. In International Conference on Learning Representations . Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017. Learned in translation: Con-

context: tool for measuring readability. Journalism Bulletin , 30(4):415–433. Erik F Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In CoNLL . Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. In Proceedings of the 48th Annual Meeting of the Association for Compu- tational Linguistics , ACL ’10, pages 384–394. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Pro- cessing Systems , pages 6000–6010. Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoen- coders. In Proceedings of the 25th international conference on Machine learning , pages 1096–1103. ACM. Alex Wang, Amanpreet Singh, Julian Michael, Fe- lix Hill, Omer Levy, and Samuel Bowman. 2018a. Glue: A multi-task benchmark and analysis platformfor natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: An- alyzing and Interpreting Neural Networks for NLP , pages 353–355. Wei Wang, Ming Yan, and Chen Wu. 2018b. Multi- granularity hierarchical attention fusion networks for reading comprehension and question answering. InProceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Lin- guistics. Alex Warstadt, Amanpreet Singh, and Samuel R Bow- man. 2018. Neural network acceptability judg- ments. arXiv preprint arXiv:1805.12471 . Adina Williams, Nikita Nangia, and Samuel R Bow- man. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In NAACL . Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google’s neural ma- chine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 . Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How transferable are features in deep neural networks? In Advances in neural information processing systems , pages 3320–3328. Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le. 2018. QANet: Combining local convolution with global self-attention for reading comprehen- sion. In ICLR . Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018. Swag: A large-scale adversarial dataset for grounded commonsense inference. In Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) . Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhut- dinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision , pages 19–27. Appendix for “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” We organize the appendix into three sections: • Additional implementation details for BERT are presented in Appendix A;• Additional details for our experiments are presented in Appendix B; and • Additional ablation studies are presented in Appendix C. We present additional ablation studies for BERT including: –Effect of Number of Training Steps; and –Ablation for Different


question: What is the main difference between BERT and previous language representation models?

answer: BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, allowing it to incorporate context from both directions. This is unlike previous language representation models that are unidirectional, which limits the choice of architectures that can be used during pre-training and could be sub-optimal for sentence-level tasks and token-level tasks such as question answering.

context: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova Google AI Language fjacobdevlin,mingweichang,kentonl,kristout g@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 andﬁne-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 allpre- 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 such as question answering, where it is crucial to incor- porate context from both directions. In this paper, we improve the ﬁne-tuning based approaches by proposing BERT: Bidirectional Encoder Representations from Transformers.

context: 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 such as question answering, where it is crucial to incor- porate context from both directions. In this paper, we improve the ﬁne-tuning based approaches by proposing BERT: Bidirectional Encoder Representations from Transformers. BERT alleviates the previously mentioned unidi- rectionality constraint by using a “masked lan- guage model” (MLM) pre-training objective, in- spired by the Cloze task (Taylor, 1953). The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the maskedarXiv:1810.04805v2 [cs.CL] 24 May 2019word based only on its context. Unlike left-to- right language model pre-training, the MLM ob- jective enables the representation to fuse the left and the right context, which allows us to pre- train a deep bidirectional Transformer. In addi- tion to the masked language model, we also use a “next sentence prediction” task that jointly pre- trains text-pair representations. The contributions of our paper are as follows: • We demonstrate the importance of bidirectional pre-training for language representations. Un- like Radford et al. (2018), which uses unidirec- tional language models for pre-training, BERT uses masked language models to enable pre- trained deep bidirectional representations. This is also in contrast to Peters et al. (2018a), which uses a shallow concatenation of independently trained left-to-right and right-to-left LMs. • We show that pre-trained representations reduce the need for many heavily-engineered task- speciﬁc architectures. BERT is the ﬁrst ﬁne- tuning based representation model that achieves state-of-the-art performance on a large suite of sentence-level andtoken-level tasks, outper- forming many task-speciﬁc architectures. • BERT advances the state of the art for eleven NLP tasks. The code and pre-trained mod- els are available at https://github.com/ google-research/bert . 2 Related Work There is a long history of pre-training general lan- guage representations, and we brieﬂy review the most widely-used approaches in this section. 2.1 Unsupervised Feature-based Approaches Learning widely applicable representations of words has been an active area of research for decades, including non-neural (Brown et al., 1992; Ando and Zhang, 2005; Blitzer et al., 2006) and neural (Mikolov et al., 2013; Pennington et al., 2014) methods. Pre-trained word embeddings are an integral part of modern NLP systems, of- fering signiﬁcant improvements over embeddings learned from scratch (Turian et al., 2010). To pre- train word embedding vectors, left-to-right lan- guage modeling objectives have been used (Mnih and Hinton, 2009), as well as objectives to dis- criminate correct from incorrect words in left and right context (Mikolov et al., 2013).These approaches have been generalized to coarser granularities, such as sentence embed- dings (Kiros et al., 2015; Logeswaran and Lee, 2018) or paragraph embeddings (Le and Mikolov, 2014). To train sentence representations, prior work has used objectives to rank candidate next sentences (Jernite et al., 2017; Logeswaran and Lee, 2018), left-to-right generation of next sen- tence words given a representation of the previous sentence (Kiros et al., 2015), or denoising auto- encoder

context: the token-level classiﬁer over the NER label set. To ablate the ﬁne-tuning approach, we apply the feature-based approach by extracting the activa- tions from one or more layers without ﬁne-tuning any parameters of BERT. These contextual em- beddings are used as input to a randomly initial- ized two-layer 768-dimensional BiLSTM before the classiﬁcation layer. Results are presented in Table 7. BERT LARGE performs competitively with state-of-the-art meth- ods. The best performing method concatenates the token representations from the top four hidden lay- ers of the pre-trained Transformer, which is only 0.3 F1 behind ﬁne-tuning the entire model. This demonstrates that BERT is effective for both ﬁne- tuning and feature-based approaches. 6 Conclusion Recent empirical improvements due to transfer learning with language models have demonstrated that rich, unsupervised pre-training is an integral part of many language understanding systems. In particular, these results enable even low-resource tasks to beneﬁt from deep unidirectional architec- tures. Our major contribution is further general- izing these ﬁndings to deep bidirectional architec- tures, allowing the same pre-trained model to suc- cessfully tackle a broad set of NLP tasks.References Alan Akbik, Duncan Blythe, and Roland V ollgraf. 2018. Contextual string embeddings for sequence labeling. In Proceedings of the 27th International Conference on Computational Linguistics , pages 1638–1649. Rami Al-Rfou, Dokook Choe, Noah Constant, Mandy Guo, and Llion Jones. 2018. Character-level lan- guage modeling with deeper self-attention. arXiv preprint arXiv:1808.04444 . Rie Kubota Ando and Tong Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research , 6(Nov):1817–1853. Luisa Bentivogli, Bernardo Magnini, Ido Dagan, Hoa Trang Dang, and Danilo Giampiccolo. 2009. The ﬁfth PASCAL recognizing textual entailment challenge. In TAC. NIST. John Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with structural correspon- dence learning. In Proceedings of the 2006 confer- ence on empirical methods in natural language pro- cessing , pages 120–128. Association for Computa- tional Linguistics. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large anno- tated corpus for learning natural language inference. InEMNLP . Association for Computational Linguis- tics. Peter F Brown, Peter V Desouza, Robert L Mercer, Vincent J Della Pietra, and Jenifer C Lai. 1992. Class-based n-gram models of natural language. Computational linguistics , 18(4):467–479. Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez- Gazpio, and Lucia Specia. 2017. Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) , pages 1–14, Vancou- ver, Canada. Association for Computational Lin- guistics. Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robin- son. 2013. One billion word benchmark for measur- ing progress in statistical language modeling. arXiv preprint arXiv:1312.3005 . Z. Chen, H. Zhang, X. Zhang, and L. Zhao. 2018. Quora question pairs. Christopher Clark and Matt Gardner. 2018. Simple and effective multi-paragraph reading comprehen- sion. In ACL.Kevin Clark, Minh-Thang Luong, Christopher D Man- ning, and Quoc Le. 2018. Semi-supervised se- quence modeling with cross-view training. In Pro- ceedings of the 2018 Conference on Empirical Meth- ods