Revisiting Self-Training for Neural Sequence Generation (1909.13788v3)

Abstract: Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's prediction (i.e. the pseudo-parallel data). While self-training has been extensively studied on classification problems, in complex sequence generation tasks (e.g. machine translation) it is still unclear how self-training works due to the compositionality of the target space. In this work, we first empirically show that self-training is able to decently improve the supervised baseline on neural sequence generation tasks. Through careful examination of the performance gains, we find that the perturbation on the hidden states (i.e. dropout) is critical for self-training to benefit from the pseudo-parallel data, which acts as a regularizer and forces the model to yield close predictions for similar unlabeled inputs. Such effect helps the model correct some incorrect predictions on unlabeled data. To further encourage this mechanism, we propose to inject noise to the input space, resulting in a "noisy" version of self-training. Empirical study on standard machine translation and text summarization benchmarks shows that noisy self-training is able to effectively utilize unlabeled data and improve the performance of the supervised baseline by a large margin.

Insights on Revisiting Self-Training for Neural Sequence Generation

The paper "Revisiting Self-Training for Neural Sequence Generation" by Junxian He, Jiatao Gu, Jiajun Shen, and Marc'Aurelio Ranzato, provides an in-depth exploration of self-training (ST) methodologies for neural sequence generation tasks, specifically addressing machine translation and text summarization domains. Although self-training is a well-established technique in supervised learning, its efficacy and mechanisms in sequence generation tasks remain insufficiently understood. The authors propose notable modifications and derive relevant insights that enhance the applicability and performance of self-training in these contexts.

Core Contributions and Findings

At its core, the paper revisits traditional self-training by examining its role in neural sequence generation. The primary finding is that self-training, when properly adjusted, shows significant promise in improving baseline performance, particularly in low-resource settings.

1. Role of Dropout as Perturbation: A significant insight from the paper is the role of dropout in improving the efficacy of self-training. Dropout introduces a form of 'perturbation' in the hidden states of the network, which encourages the model to produce similar outputs for inputs that are semantically close. The inclusion of dropout was critical in preventing the model from collapsing into local optima and hence was pivotal for the observed performance gains.
2. Noisy Self-Training (NST): Building on the findings that perturbations aid performance, the authors propose a further modification, dubbed 'noisy self-training'. This involves introducing noise into the input space, which they find further enhances the ability of the model to leverage unlabeled data. This method empirically demonstrated superior performance over standard self-training, especially when synthetic perturbations or paraphrases were introduced into the input training set.
3. Robustness Across Different Datasets: The authors conducted experiments on datasets of varying sizes and resource levels, such as the high-resource WMT14 English-German and the low-resource FloRes English-Nepali datasets. The conducted experiments consistently showed that noisy ST could improve over baseline models by a notable margin, demonstrating increased robustness and adaptability across domains.
4. Comparative Analysis with Back-Translation (BT): Across several experiments, self-training approaches were compared to back-translation, a commonly utilized semi-supervised learning technique in machine translation. Notably, NST showcased competitive performance with BT, achieving comparable or superior results in certain configurations. However, the domain mismatch was an identified factor that could influence relative performance gains between BT and NST, notably highlighted in various subtasks and datasets.

Implications and Future Directions

The findings of this paper have several implications for future research and development in AI and machine learning:

• Enhancements in Semi-Supervised Learning: The use of perturbations, both via dropout and input noise, illustrates potential new avenues for enhancing semi-supervised learning techniques beyond the traditionally confined settings of static label prediction models.
• Effective Utilization of Unlabeled Data: By showing that NST can significantly leverage unlabeled data to improve model performance, this work underscores the importance of exploring various perturbation strategies to better harness large, unlabeled datasets that are increasingly available in numerous tasks and industries.
• Design Strategies for Low-Resource Languages: Given the demonstrated effectiveness of NST in low-resource settings such as FloRes, these methods promise impactful advancements for tasks involving low-resource languages where training data is scarce, allowing for more democratized accessibility and use of advanced AI technologies.

Conclusion

In summary, this paper significantly enriches the understanding of self-training's role in neural sequence generation and proposes modifications that potentially transform its effectiveness. The introduction of noise, combined with a rigorous analysis of dropout mechanisms, provides a new lens through which neural network stabilization and performance can be enhanced. Future research may delve into more generalized frameworks for perturbation or investigate further cross-domain applications where noisy self-training could be most beneficial.