# An Empirical Investigation of Multi-bridge Multilingual NMT models

arXiv: https://arxiv.org/abs/2110.07304 · HTML: https://mlsanity.com/paper/2110.07304/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations](https://mlsanity.com/paper/1906.01181/) (2019)
- [The Missing Ingredient in Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/1903.07091/) (2019)
- [Multilingual Neural Machine Translation with Language Clustering](https://mlsanity.com/paper/1908.09324/) (2019)
- [Complete Multilingual Neural Machine Translation](https://mlsanity.com/paper/2010.10239/) (2020)
- [Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders](https://mlsanity.com/paper/2004.06575/) (2020)
- [Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2011.01703/) (2020)
- [Improving Zero-Shot Translation of Low-Resource Languages](https://mlsanity.com/paper/1811.01389/) (2018)
- [Revisiting Modularized Multilingual NMT to Meet Industrial Demands](https://mlsanity.com/paper/2010.09402/) (2020)

## Built on by (most cited first, 2 of 2)

- [Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication](https://mlsanity.com/paper/2404.15166/) (2024)
- [Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languages](https://mlsanity.com/paper/2308.05574/) (2023)

## Abstract (arXiv)

In this paper, we present an extensive investigation of multi-bridge, many-to-many multilingual NMT models (MB-M2M) ie., models trained on non-English language pairs in addition to English-centric language pairs. In addition to validating previous work which shows that MB-M2M models can overcome zeroshot translation problems, our analysis reveals the following results about multibridge models: (1) it is possible to extract a reasonable amount of parallel corpora between non-English languages for low-resource languages (2) with limited non-English centric data, MB-M2M models are competitive with or outperform pivot models, (3) MB-M2M models can outperform English-Any models and perform at par with Any-English models, so a single multilingual NMT system can serve all translation directions.