RESEARCH

NLP

Pre-training via Paraphrasing

December 06, 2020

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.

Download the Paper

AUTHORS

Written by

Michael Lewis

Armen Aghajanyan

Gargi Ghosh

Luke Zettlemoyer

Marjan Ghazvininejad

Sida Wang

Publisher

NeurIPS

Related Publications

June 14, 2024

NLP

How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Scott Yih, Xilun Chen

June 14, 2024

June 14, 2024

NLP

SYSTEMS RESEARCH

LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding

Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Nas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed Aly, Beidi Chen, Carole-Jean Wu

June 14, 2024

June 13, 2024

NLP

Know When To Stop: A Study of Semantic Drift in Text Generation

Ava Spataru, Eric Hambro, Lena Voita, Nicola Cancedda

June 13, 2024

May 24, 2024

SPEECH & AUDIO

NLP

DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation

Zhe Liu

May 24, 2024

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.