Research

NLP

Cross-lingual Retrieval for Iterative Self-Supervised Training

December 6, 2020

Abstract

Recent studies have demonstrated the cross-lingual alignment ability of multilingual pretrained language models. In this work, we found that the cross-lingual alignment can be further improved by training seq2seq models on sentence pairs mined using their own encoder outputs. We utilized these findings to develop a new approach -- cross-lingual retrieval for iterative self-supervised training (CRISS), where mining and training processes are applied iteratively, improving cross-lingual alignment and translation ability at the same time. Using this method, we achieved state-of-the-art unsupervised machine translation results on 9 language directions with an average improvement of 2.4 BLEU, and on the Tatoeba sentence retrieval task in the XTREME benchmark on 16 languages with an average improvement of 21.5% in absolute accuracy. Furthermore, CRISS also brings an additional 1.8 BLEU improvement on average compared to mBART, when finetuned on supervised machine translation downstream tasks.

Download the Paper

AUTHORS

Research Topics

Natural Language Processing

Related Publications

November 16, 2022

NLP

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Kushal Tirumala, Aram H. Markosyan, Armen Aghajanyan, Luke Zettlemoyer

November 16, 2022

October 31, 2022

NLP

Autoregressive Search Engines: Generating Substrings as Document Identifiers

Fabio Petroni, Giuseppe Ottaviano, Michele Bevilacqua, Patrick Lewis, Scott Yih, Sebastian Riedel

October 31, 2022

December 06, 2020

NLP

Pre-training via Paraphrasing

Michael Lewis, Armen Aghajanyan, Gargi Ghosh, Luke Zettlemoyer, Marjan Ghazvininejad, Sida Wang

December 06, 2020

November 30, 2020

NLP

Where Are You? Localization from Embodied Dialog

Dhruv Batra, Devi Parikh, Meera Hahn, Jacob Krantz, James Rehg, Peter Anderson, Stefan Lee

November 30, 2020

April 30, 2018

NLP

Speech & Audio

Identifying Analogies Across Domains | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

November 01, 2018

NLP

Computer Vision

Non-Adversarial Unsupervised Word Translation | Facebook AI Research

Yedid Hoshen, Lior Wolf

November 01, 2018

December 02, 2018

NLP

Computer Vision

One-Shot Unsupervised Cross Domain Translation | Facebook AI Research

Sagie Benaim, Lior Wolf

December 02, 2018

June 30, 2019

NLP

Variational Training for Large-Scale Noisy-OR Bayesian Networks | Facebook AI Research

Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth

June 30, 2019

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.