In the many realms inside the artificial intelligence industry, we understand the value and need benchmarking offers. Here at aiXplain, we are driving this initiative starting with benchmarking machine translation (MT) services on a range of different industry metrics marking for accuracy, speed, and more. MT service providers and specialists can easily access the benefits of machine translation benchmarking for quality and latency of their own models to assess their performance against alternatives in the industry.

What are the key challenges being solved with benchmarking for MT on aiXplain? 

Inability to track MT model performance against others 

Due to the vast ecosystem of MT service providers, it is increasingly becoming difficult to effectively track how a MT model compares against others without any benchmarking system in place. In this rapidly changing landscape of multiple MT service providers, there is a strong need for infrastructure with benchmarking capabilities allowing MT service providers to improve their system performance by comparing them against others. 

Difficult for MT service providers to build their own benchmarking system

In order to build their own system, MT service providers would need to build the whole infrastructure including subscription and integration to other MT services. This would require: 

  1. Collection of MT data
  2. Subscription to each MT service provider
  3. Custom integration with each MT service
  4. Getting prediction of each MT service for the data sample
  5. Implementation and calculation of MT metrics for the sample
  6. Development of an assessment with visualization
  7. Need for resources such as time and money

Benefits of benchmarking for MT

Receive an objective and accurate performance assessment

As aiXplain members, MT service providers will receive objective and accurate performance assessments on their models compared to industry measuring for metrics such as speed, accuracy and more. 

Understand areas of improvement for MT models  

aiXplain-provided assessments highlight the areas of improvements for MT service providers enabling them to better understand their strengths and weaknesses as they strive for continuous improvement.

Ability to continuously monitor for potential issues

With aiXplain, MT service providers also receive the additional benefit of continuously monitoring their models for problems that may be causing customer dissatisfaction allowing for a seamless debugging of models. 

Use cases for benchmarking for Machine Translation

  1. MT service providers can easily access the performance of their own models by understanding its capabilities and limitations to determine the area of focus for improvement of their models
  2. MT service providers can compare their models with others offered in the market on a range of select metrics (accuracy, speed, availability)

Organizations looking for benchmarking services for their models can receive a detailed, objective performance assessment on aiXplain. Join our private beta today to get started!

*benchmark scoring examples: Bleu, CLSSS

Pin It on Pinterest