MVP: Multi-task Supervised Pre-training for Natural Language Generation

Tianyi Tang, Junyi Li, Xin Zhao, Ji-Rong Wen

Keywords: Natural language generation, pretrained language models, multi-task learning, prompt learning

Abstract: Pre-trained language models (PLMs) have achieved remarkable success in natural language generation (NLG) tasks. Up to now, most NLG-oriented PLMs are pre-trained in an unsupervised manner using the large-scale general corpus. In the meanwhile, an increasing number of models pre-trained with labeled data (i.e., “supervised pre-training”) showcase superior performance compared to unsupervised pre-trained models. Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation. We collect a large-scale natural language generation corpus, MVPCorpus, from $77$ datasets over $11$ diverse NLG tasks. Then we unify these examples into a general text-to-text format to pre-train the text generation model MVP in a supervised manner. For each task, we further pre-train specific soft prompts to stimulate the model’s capacity to perform a specific task. Extensive experiments have demonstrated the effectiveness and generalizability of our MVP model in a number of NLG tasks, which achieves state-of-the-art performance on $13$ out of $17$ datasets.

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TL;DR: We pre-train a model MVP and task-specific prompts for natural language generation tasks with our collected labeled corpora MVPCorpus.

Community Implementations: [ 13 code implementations](https://www.catalyzex.com/paper/mvp-multi-task-supervised-pre-training-for/code)