 This search requires a measure that captures the semantic similarity between sentences in different languages.

Mining parallel data with LASER

We apply a filtering step to remove sentences of greater than 50% punctuation. The data is then deduplicated, and we remove any sentence that appears in any validation or test dataset – even if it is associated with another language pair. 

Finally, we apply length and language-specific filtering

Postprocessing

Supervised translation systems rely on large quantities of parallel sentences

Hence, we use mining technique

University of Michigan - Ann Arbor

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

How to Build a Many-to-Many Parallel Dataset for 100 Languages

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

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

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

The evaluation of a many-to-many multilingual translation model can include a human evaluation study to assess translation quality. This study can focus on translation directions that do not involve English, particularly between languages commonly spoken in the same region (e.g., Japanese-Chinese, Hindi-Tamil, and Russian-Ukrainian).

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