Ankur Bapna

Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

2020 · arXiv (Cornell University)

Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The …

mSLAM: Massively multilingual joint pre-training for speech and text

2022 · arXiv (Cornell University)

We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unlabeled speech and text in multiple languages. mSLAM combines w2v-BERT …

Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents

2021 · arXiv (Cornell University)

Document-level neural machine translation (DocNMT) achieves coherent translations by incorporating cross-sentence context. However, for most language pairs there's a shortage of parallel documents, although parallel sentences are readily available. In this paper, we study whether …

The Missing Ingredient in Zero-Shot Neural Machine Translation

2019 · arXiv (Cornell University)

Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages. Despite various approaches to train such models, they have difficulty with zero-shot translation: translating between language pairs that were …

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2019 · arXiv (Cornell University)

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, …

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

2019 · arXiv (Cornell University)

We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model …

Simple, Scalable Adaptation for Neural Machine Translation

2019

Ankur Bapna, Orhan Firat. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

2022 · Transactions of the Association for Computational Linguistics

Abstract With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, Web-mined text datasets covering hundreds of languages. We manually audit the quality …