Competence-based Curriculum Learning for Neural Machine Translation (1903.09848v2)

Abstract: Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU.

• The paper introduces a competence-based curriculum approach that sequences training examples by difficulty and model competence.
• It leverages sentence length and word rarity as heuristics, with competence evolving via linear or square root functions.
• Experiments reveal up to 70% reduced training time and a 2.2 BLEU score improvement, especially for Transformer models.

Competence-based Curriculum Learning for Neural Machine Translation

The paper "Competence-based Curriculum Learning for Neural Machine Translation" introduces a sophisticated methodology aimed at enhancing the training efficiency and performance of Neural Machine Translation (NMT) systems. With an emphasis on reducing both training time and dependency on complex heuristics, this approach offers a structured curriculum learning framework tailored to NMT.

In NMT, achieving optimal performance typically necessitates large-scale neural networks, which are not only computationally expensive to train but also require meticulous tuning of hyperparameters such as learning rates and batch sizes. This paper proposes a curriculum learning strategy designed to alleviate these challenges by sequencing the presentation of training data based on the perceived difficulty of sentences and the current competence level of the model.

Core Methodology

The framework's core premise lies in dynamically adjusting the accessibility of training examples based on two key metrics:

1. Difficulty: Defined as a function of sentence characteristics, with two primary heuristics considered—sentence length and word rarity. These factors intuitively influence the complexity of translating a sentence.
2. Competence: This term quantifies the progression of learning in the model. It represents the subset of training data the model is considered adept enough to learn from at any given time, gradually encompassing more challenging examples.

The competence dynamically evolves according to predefined linear or square root functions, which determine the rate at which new, more difficult examples are introduced to the training regime.

Experimental Evaluation

The proposed curriculum learning strategy was applied to standard RNN and Transformer-based NMT models across three well-established datasets: IWSLT-15 En$\to$Vi, IWSLT-16 Fr$\to$En, and WMT-16 En$\to$De. The experimental outcomes indicated significant improvements in both training efficiency and translation accuracy, particularly for Transformer models. The paper reports up to a 70% reduction in training time and BLEU score improvements of up to 2.2 points.

Notably, the paper demonstrates that, while both RNNs and Transformers benefit from this curriculum approach, the gains are more pronounced for Transformers. This aligns with the hypothesis that more sophisticated models suffer from instability without tailored training schedules, an issue that the curriculum approach effectively mitigates.

Implications and Future Directions

The introduction of competence-based curriculum learning offers a promising direction for improving NMT systems, reducing reliance on extensive hyperparameter tuning and complex heuristics. The results are compelling enough to suggest potential adaptations of curriculum learning strategies in various machine learning domains beyond NMT.

Future work could explore additional difficulty metrics, potentially including syntactic complexity or semantic congruity between source and target texts. Furthermore, the curriculum framework's adaptability to new languages and multilingual settings presents an exciting avenue for research, particularly given the dynamic nature of linguistic resources and the varying availability of parallel corpora. Integrating more adaptive competence models that react to live learner feedback could also refine the curriculum's effectiveness, presenting an ongoing challenge and an opportunity for innovation in the field. This research lays a foundational step towards more efficient, intuitive, and high-performing NMT systems.