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Unified Language Model Pre-training for Natural Language Understanding and Generation
Li Dong · Nan Yang · Wenhui Wang · Furu Wei · Xiaodong Liu · Yu Wang · Jianfeng Gao · Ming Zhou · Hsiao-Wuen Hon

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #140

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm.

Author Information

Li Dong (Microsoft Research)
Nan Yang (Microsoft Research Asia)
Wenhui Wang (Microsoft Research)
Furu Wei (Microsoft Research Asia)
Xiaodong Liu (Microsoft)
Yu Wang (Microsoft Research)
Jianfeng Gao (Microsoft Research, Redmond, WA)
Ming Zhou (Microsoft Research)
Hsiao-Wuen Hon (Microsoft Research)

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