Naveen Arivazhagan

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 …

Language-agnostic BERT Sentence Embedding

2020 · arXiv (Cornell University)

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically …

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 …

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 …

Language-agnostic BERT Sentence Embedding

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored. We systematically …