Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
arXiv:2602.17287v1 Announce Type: new Abstract: Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-token prediction training strategy, while widely adopted in practice, may lead to overlooked artifacts such as representation collapse. Previous works have shown that this problem is especially pronounced in the representation of the deeper Transformer layers, where it often fails to efficiently utilize the geometric space. Representation collapse is even more evident in end-to-end training of continuous-output neural machine translation, where the trivial solution would be to set all vectors to the same value. In this work, we analyze the dynamics of representation collapse at different levels of discrete and continuous NMT transformers throughout training. We incorporate an existing regularization method based on angular dispersion and demonstrate empirically t
arXiv:2602.17287v1 Announce Type: new Abstract: Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-token prediction training strategy, while widely adopted in practice, may lead to overlooked artifacts such as representation collapse. Previous works have shown that this problem is especially pronounced in the representation of the deeper Transformer layers, where it often fails to efficiently utilize the geometric space. Representation collapse is even more evident in end-to-end training of continuous-output neural machine translation, where the trivial solution would be to set all vectors to the same value. In this work, we analyze the dynamics of representation collapse at different levels of discrete and continuous NMT transformers throughout training. We incorporate an existing regularization method based on angular dispersion and demonstrate empirically that it not only mitigates collapse but also improves translation quality. Furthermore, we show that quantized models exhibit similar collapse behavior and that the benefits of regularization are preserved even after quantization.
Executive Summary
This article examines the phenomenon of representation collapse in neural machine translation (NMT) models, specifically the Transformer architecture. The authors propose the incorporation of angular dispersion as a regularization method to mitigate collapse and improve translation quality. Empirical results demonstrate the effectiveness of this approach in both discrete and continuous NMT models. Notably, the benefits of regularization are preserved even after quantization. The study contributes to the understanding of NMT dynamics and offers a novel solution to representation collapse, a previously overlooked issue in deep learning-based translation models.
Key Points
- ▸ Representation collapse is a critical issue in NMT models, particularly in deeper Transformer layers.
- ▸ Angular dispersion is proposed as a regularization method to mitigate representation collapse.
- ▸ Empirical results demonstrate improved translation quality with angular dispersion regularization.
- ▸ Quantized models exhibit similar collapse behavior, but benefits of regularization are preserved.
Merits
Strength
The study proposes a novel solution to representation collapse, a previously overlooked issue in NMT models.
Methodological rigor
The authors employ empirical methods to demonstrate the effectiveness of angular dispersion regularization.
Demerits
Limitation
The study focuses on a specific NMT architecture (Transformer) and may not generalize to other architectures.
Quantization impact
The study assumes that quantization does not significantly impact the benefits of regularization, but further investigation is needed to confirm this assumption.
Expert Commentary
The article presents a timely and relevant contribution to the field of NMT research. The proposed regularization method shows promise in mitigating representation collapse, a previously overlooked issue in deep learning-based translation models. However, further investigation is needed to confirm the generalizability of the results to other NMT architectures and to explore the impact of quantization on regularization benefits. Additionally, the study highlights the need for further research on representation collapse and its potential impact on NMT performance. As the field continues to evolve, this study provides a valuable insight into the dynamics of NMT models and offers a potential solution to improve translation quality.
Recommendations
- ✓ Future research should investigate the generalizability of the proposed regularization method to other NMT architectures.
- ✓ Additional studies should explore the impact of quantization on regularization benefits and representation collapse.
