[Paper Review] Curb Your Carbon Emissions: Benchmarking Carbon Emissions in Machine Translation

Mirza Yusuf, Praatibh Surana+2 more|arXiv (Cornell University)|Sep 26, 2021

Natural Language Processing Techniques17 references4 citations

TL;DR

This paper benchmarks carbon emissions across machine translation models for multiple language pairs, identifying computational inefficiencies in training pipelines. It analyzes model components to propose optimizations that reduce environmental impact, revealing significant variation in emissions based on language pair complexity and model architecture.

ABSTRACT

In recent times, there has been definitive progress in the field of NLP, with its applications growing as the utility of our language models increases with advances in their performance. However, these models require a large amount of computational power and data to train, consequently leading to large carbon footprints. Therefore, it is imperative that we study the carbon efficiency and look for alternatives to reduce the overall environmental impact of training models, in particular large language models. In our work, we assess the performance of models for machine translation, across multiple language pairs to assess the difference in computational power required to train these models for each of these language pairs and examine the various components of these models to analyze aspects of our pipeline that can be optimized to reduce these carbon emissions.

Motivation & Objective

• To quantify and compare the carbon emissions associated with training machine translation models across diverse language pairs.
• To identify computational bottlenecks in the training pipeline that contribute disproportionately to carbon emissions.
• To evaluate how model architecture and data requirements influence environmental impact during training.
• To propose actionable optimization strategies for improving carbon efficiency in NLP model development.

Proposed method

• Training state-of-the-art machine translation models on multiple language pairs to measure computational resource usage.
• Tracking energy consumption and carbon dioxide equivalent (CO2e) emissions throughout the training process.
• Analyzing model components such as attention mechanisms, vocabulary size, and sequence length for their contribution to emissions.
• Comparing emissions across different model architectures and data scales to identify high-impact factors.
• Using standardized metrics to normalize performance and emissions for cross-language pair comparison.
• Applying optimization techniques such as model distillation and mixed-precision training to assess emission reduction potential.

Experimental results

Research questions

• RQ1 How do carbon emissions vary across different language pairs when training machine translation models?
• RQ2 Which components of the model training pipeline contribute most significantly to carbon emissions?
• RQ3 To what extent can model architecture and data size influence the environmental cost of training?
• RQ4 What are the most effective optimization strategies for reducing carbon emissions in machine translation training?

Key findings

• Significant variation in carbon emissions was observed across language pairs, with lower-resource pairs generally requiring more emissions per parameter.
• Attention mechanisms and long sequence lengths were identified as major contributors to increased energy consumption.
• Vocabulary size and data preprocessing steps also played a notable role in amplifying computational demands.
• Model distillation and mixed-precision training reduced emissions by up to 30% in tested configurations.
• The choice of training hardware and optimization hyperparameters had a measurable impact on overall carbon footprint.
• High-resource language pairs exhibited better performance-to-emission ratios compared to low-resource counterparts.