Unified Language Model Pre-training for Natural Language Understanding and Generation
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.
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Datasets
GLUE
SQuAD
CNN/Daily Mail
CoQA
Results from the Paper
Ranked #2 on
Generative Question Answering
on CoQA
(using extra training data)
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Uses Extra Training Data |
Benchmark |
|---|---|---|---|---|---|---|---|
| Abstractive Text Summarization | CNN / Daily Mail | UniLM | ROUGE-1 | 43.08 | # 25 | ||
| ROUGE-2 | 20.43 | # 21 | |||||
| ROUGE-L | 40.34 | # 24 | |||||
| Document Summarization | CNN / Daily Mail | UniLM (Abstractive Summarization) | ROUGE-1 | 43.08 | # 13 | ||
| ROUGE-2 | 20.43 | # 10 | |||||
| ROUGE-L | 40.34 | # 11 | |||||
| Generative Question Answering | CoQA | UniLM | F1-Score | 82.5 | # 2 | ||
| Text Summarization | GigaWord | UniLM | ROUGE-1 | 38.90 | # 16 | ||
| ROUGE-2 | 20.05 | # 14 | |||||
| ROUGE-L | 36.00 | # 17 | |||||
| Question Generation | SQuAD1.1 | UniLM | BLEU-4 | 22.78 | # 8 | ||
| METEOR | 25.1 | # 6 | |||||
| ROUGE-L | 51.1 | # 6 |
