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One Sentence Abstract

This paper introduces Curvature Aware Task Scaling (CATS), a principled optimization algorithm that improves multilingual training by adaptively rescaling gradients, resulting in consistent gains for low-resource languages without compromising high-resource performance, and demonstrating robustness to overparameterization and large batch size training.

Simplified Abstract

This research focuses on improving multilingual translation models, which can work effectively with languages that have less data available. The traditional way of training these models often doesn't work well with languages that have less data, although it's great for languages with a lot of data. The researchers discovered that this problem is because of 'data imbalance' among languages.

To solve this issue, they've developed a new method called 'Curvature Aware Task Scaling' or CATS. This method adaptively adjusts the learning process for different languages, making sure that each language gets the right amount of attention during the training process. They tested their new method on different benchmarks with varying degrees of data imbalance, and found that CATS significantly improved the translation quality for the less data-rich languages without hurting the ones with more data.

This new approach is also strong even when the model is over-sized and trained in large batches, making it very promising for future massive multilingual models.

In simple terms, the study is about making machine translation work better for languages that have less data available. They found that a common issue in machine learning is that the models perform better for languages with a lot of data than for those with less data. They then developed a new method, CATS, to solve this issue by adjusting how the model learns from different languages. This new method improved the translation quality for languages with less data, while not hurting the ones with more data. The best part is, this method works even in more complex situations.

Study Fields

Main fields:

• Natural Language Processing (NLP)
• Multilingual models
• Crosslingual transfer
• Optimization algorithms

Subfields:

• Data imbalance among languages
• Low-resource languages
• High-resource languages
• Loss landscape geometry
• Generalization
• Curvature Aware Task Scaling (CATS) algorithm
• Benchmark evaluation (TED, WMT, OPUS-100)
• Overparameterization
• Large batch size training

Study Objectives

• Investigate the effectiveness of training multilingual models, particularly in low-resource languages.
• Identify the issues caused by data imbalance among languages in multilingual training.
• Analyze the limitations of common training methods that address data imbalance in multilingual models.
• Propose a principled optimization algorithm, Curvature Aware Task Scaling (CATS), to improve multilingual optimization.
• Evaluate the performance of the proposed algorithm on common benchmarks (TED, WMT, and OPUS-100) with varying degrees of data imbalance.
• Demonstrate the improvement of low-resource languages using CATS without hurting high-resource languages.
• Highlight the robustness of CATS under overparameterization and large batch size training, making it suitable for massive multilingual models.

Conclusions

• Data imbalance among languages in multilingual training can cause optimization tensions between high resource and low resource languages.
• Common training methods, such as upsampling low resources, may not robustly optimize population loss and can lead to underfitting or overfitting of languages.
• A principled optimization algorithm, Curvature Aware Task Scaling (CATS), is proposed to adaptively rescales gradients from different tasks, guiding multilingual training to low-curvature neighborhoods with uniformly low loss for all languages.
• CATS effectively improved multilingual optimization and consistently demonstrated gains on low-resource languages (+0.8 to +2.2 BLEU), without hurting high-resource languages.
• CATS is robust to overparameterization and large batch size training, making it a promising training method for massive multilingual models that truly improve low-resource languages.

References