核心概念
モジュール式の機械翻訳システムの効率的なトレーニングとスケーリングに焦点を当てたMAMMOTHツールキットが紹介されました。
要約
1. Abstract:
NLP is transitioning from large monolithic models to modular systems for scalability.
MAMMOTH toolkit enables training of modular machine translation systems efficiently.
Utilizes A100 and V100 NVIDIA GPUs for training across computation clusters.
2. Introduction:
Large neural networks are unsustainable due to data scarcity and scalability issues.
Modularity in multilingual NLP addresses challenges of interference and limited model capacity.
MAMMOTH toolkit aims to fill the gap in designing and handling modular models.
3. What is modularity?
Modularity can be viewed as sparsity or conditional computation.
Language-specific encoders and decoders enhance translation efficiency.
Dynamic selection of modules leads to efficient inference by avoiding unnecessary computations.
4. Toolkit design:
Requirements include broad architecture coverage and efficient training across compute nodes.
Design principles focus on task-based configurations for specific model behaviors.
Major components include bin, transforms, distributed, inputters, modules, models, translate, and utils submodules.
5. Performances:
Utilizes Europarl dataset for benchmarking performance on V100 and A100 clusters.
Achieves nearly ideal scaling with different parameter-sharing schemes.
Environmental costs measured in a carbon-neutral data center.
6. Conclusions:
MAMMOTH toolkit is publicly available under a CC-BY license for developers and researchers.
Future developments include interfacing with HuggingFace framework and OPUS ecosystem.