The first step of establishing a Many-to-Many dataset is to select 100 languages for which there already exist high-quality, annotated datasets that can be used for model evaluation.

                 
       Therefore, We cover a diversity of scripts and resource levels (as shown in Table 1) to have high coverage of languages worldwide. (widely spoken language)

Language selection

Supervised translation systems rely on large quantities of parallel sentences

Hence, we use mining technique

Covering the Language Matrix

Goal: 

is to build a single model capable of translating 9, 900 language directions covering 100 languages. 

Challenge: 

This creates several challenges for models with insufficient capacity to capture that many languages and scripts adequatel

Components for Scaling Multilingual Translation Models

It ends with a human evaluation study to understand the quality of our model translations.

We focus on 20 different directions, none of them involving English. We include languages commonly spoken in the same region, such as Japanese-Chinese, Hindi-Tamil, and RussianUkrainian

Human Evaluation on model translation process

We use publicly available evaluation datasets to evaluate the performance of all of our models. 

To cover our set of 100 languages and 2200 direction

Evaluation according to existing models

1) Language selection \n 
2) Evaluation
3) Covering the Language Matrix by Mining

University of Michigan - Ann Arbor

1) Mechanism
2) Data collection
3) Methods

English-Centric Multilingual Machine Translation

Background: The quantity of data in a Many-to-Many dataset increases quadratically with the number of languages, making neural networks with standard capacity underfit rapidly.
What they did outstandingly: "we leverage progress in scaling (Kaplan et al., 2020; Arora et al., 2018) to train models that are over 50 times larger than current bilingual models with model parallelism"

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