Pre-training via Paraphrasing

TL;DR

MARGE is a novel unsupervised pre-training method for sequence-to-sequence models that leverages multi-lingual multi-document paraphrasing to improve performance across various NLP tasks without task-specific training.

Contribution

It introduces MARGE, a pre-training approach that jointly learns retrieval and reconstruction, capturing paraphrasing, translation, and summarization for strong zero-shot and fine-tuned performance.

Findings

Achieves BLEU scores up to 35.8 in document translation without task-specific training.

Demonstrates strong zero-shot performance on multiple NLP tasks.

Fine-tuning further enhances performance across discriminative and generative tasks.

Abstract

We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the dominant masked language modeling paradigm, where we self-supervise the reconstruction of target text by retrieving a set of related texts (in many languages) and conditioning on them to maximize the likelihood of generating the original. We show it is possible to jointly learn to do retrieval and reconstruction, given only a random initialization. The objective noisily captures aspects of paraphrase, translation, multi-document summarization, and information retrieval, allowing for strong zero-shot performance on several tasks. For example, with no additional task-specific training we achieve BLEU scores of up to 35.8 for document translation. We further show that fine-tuning gives strong performance on a range of discriminative and generative tasks in many languages, making MARGE the most generally applicable pre-training method to date.