ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

• URL: http://arxiv.org/abs/2001.11314v3
• Date: Mon, 8 Jun 2020 08:09:33 GMT
• Title: ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
• Authors: Dongling Xiao, Han Zhang, Yukun Li, Yu Sun, Hao Tian, Hua Wu and Haifeng Wang
• Abstract summary: ERNIE-GEN is an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework. It bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. It trains the model to predict semantically-complete spans consecutively rather than predicting word by word.
• Score: 44.21363470798758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks. To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. To make generation closer to human writing patterns, this framework introduces a span-by-span generation flow that trains the model to predict semantically-complete spans consecutively rather than predicting word by word. Unlike existing pre-training methods, ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data, which enhances the correlation between encoder and decoder. Experimental results demonstrate that ERNIE-GEN achieves state-of-the-art results with a much smaller amount of pre-training data and parameters on a range of language generation tasks, including abstractive summarization (Gigaword and CNN/DailyMail), question generation (SQuAD), dialogue generation (Persona-Chat) and generative question answering (CoQA).

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