Lost in Translation: Why Machine Translation Models Forget How to Speak

Author: Denis Avetisyan

A new study reveals how neural machine translation systems can lose representational diversity, and demonstrates a method to preserve translation quality by maximizing the angular separation of decoder embeddings.

Promoting angular dispersion in representation spaces mitigates representation collapse in Transformer models, improving performance even with quantization or continuous output.

Despite the demonstrated success of Transformer-based neural machine translation, particularly with large datasets, a subtle form of model degradation known as representation collapse can limit performance. This paper, ‘Representation Collapse in Machine Translation Through the Lens of Angular Dispersion’, investigates this phenomenon-where model representations become overly concentrated-and proposes a solution centered on maximizing angular dispersion in embedding spaces. Empirical results demonstrate that encouraging greater geometric separation between representations not only mitigates collapse across discrete and continuous NMT models, but also improves translation quality and maintains benefits even after quantization. Could promoting representational diversity be a broadly applicable principle for enhancing the robustness and generalization of deep neural networks?

The Inevitable Decay: Charting the Landscape of Neural Translation

Neural Machine Translation (NMT) has fundamentally reshaped the field of language processing, largely due to the advent of the Transformer architecture. Prior systems relied heavily on recurrent or convolutional neural networks, often struggling with long-range dependencies and parallelization. The Transformer, however, introduced a novel attention mechanism allowing the model to weigh the importance of different input words when generating translations, effectively capturing contextual relationships regardless of distance. This innovation enabled significant improvements in translation quality, moving beyond phrase-based statistical methods and achieving near human-level performance on certain language pairs. Consequently, NMT now powers many widely-used translation tools, facilitates cross-lingual communication, and is a core component in applications ranging from global content localization to real-time language assistance.

Neural Machine Translation systems, while achieving remarkable fluency and accuracy, are susceptible to a phenomenon termed ‘representation collapse’. This occurs when the model fails to adequately differentiate between various input sequences, effectively mapping them to similar or identical vector representations within its internal processing layers. Consequently, the model’s ability to capture nuanced meanings and generate diverse outputs diminishes, leading to performance degradation. The core issue isn’t a failure to translate, but a loss of expressiveness – the system struggles to maintain distinct identities for different inputs, ultimately limiting its capacity to handle complex linguistic structures and subtle semantic variations. Addressing this collapse is therefore paramount for building truly robust and reliable machine translation systems capable of handling the full spectrum of human language.

Representation collapse in neural machine translation presents as two primary phenomena that degrade model performance. Complete collapse signifies an extreme scenario where the model’s embedding space loses all discriminatory power, forcing all input sequences to map to identical vector representations – effectively rendering the system unable to distinguish between different meanings. More commonly, dimensional collapse occurs, wherein the model utilizes only a subset of the available embedding dimensions; while not all vectors converge to the same point, the effective dimensionality of the representation space is drastically reduced, limiting the model’s capacity to capture nuanced linguistic information and leading to impoverished translations. Both forms of collapse indicate a failure in the model’s learning process, hindering its ability to generalize and accurately process diverse language inputs.

The pursuit of robust and reliable neural machine translation (NMT) systems hinges on effectively addressing the phenomenon of representation collapse. This issue, where nuanced input data is flattened into similar vector representations, significantly degrades performance by limiting the model’s ability to distinguish between different meanings and contexts. While NMT, particularly systems built on the Transformer architecture, has achieved remarkable progress, its susceptibility to both complete and dimensional collapse introduces vulnerabilities that hinder its practical application. Mitigating this collapse isn’t merely about improving accuracy scores; it’s about building systems capable of handling the inherent ambiguity and complexity of natural language, ensuring consistent and dependable translations across diverse linguistic inputs, and ultimately, fostering trust in machine-translated content.

Measuring the Void: Metrics for Assessing Representational Integrity

Spherical Variance is a metric used to quantify representation collapse by measuring the spread of vectors in the learned representation space; a higher spherical variance indicates greater diversity among the vectors. This metric is calculated as the trace of the covariance matrix of the normalized vectors, providing a scalar value representing the overall spread. Empirical results demonstrate a positive correlation between effective regularization techniques and increases in spherical variance, suggesting that regularization can mitigate representation collapse by encouraging more diverse and informative representations. Specifically, as regularization strength increases, vectors tend to distribute more evenly on the unit hypersphere, resulting in a demonstrably higher spherical variance value.

Average Cosine Similarity is calculated as the mean of the cosine similarity scores between all pairs of vectors within a learned representation. The cosine similarity between two vectors $\vec{x}$ and $\vec{y}$ is computed as $\frac{\vec{x} \cdot \vec{y}}{||\vec{x}|| \cdot ||\vec{y}||}$ , resulting in a value between -1 and 1, where 1 indicates perfect similarity and 0 indicates orthogonality. A lower average cosine similarity across the entire representation signifies that the vectors are, on average, less aligned with each other, implying greater diversity in the learned features and potentially mitigating representation collapse. Values approaching zero suggest a more uniformly distributed representation space, while values close to one indicate redundancy and a potential lack of representational capacity.

Rényi Entropy, a generalization of Shannon Entropy, quantifies the randomness and diversity of the learned representation space by measuring the information contained within the distribution of vectors. Unlike simpler metrics, Rényi Entropy, parameterized by α, allows for varying sensitivity to different parts of the distribution; values of $\alpha > 1$ emphasize common features while values less than 1 prioritize rare features. Empirical results demonstrate that effective regularization techniques, such as adding noise or employing spectral normalization, consistently increase Rényi Entropy values, indicating a more uniformly distributed and therefore more diverse representation space, and mitigating the effects of representation collapse where vectors converge to a limited subspace.

Quantitative assessment of learned representations is crucial for evaluating model performance and identifying issues like representation collapse. Metrics such as Spherical Variance, Average Cosine Similarity, and Rényi Entropy offer specific, measurable data points regarding the diversity and distribution of vectors within the representation space. High Spherical Variance and low Average Cosine Similarity generally indicate a more dispersed and informative representation. Rényi Entropy provides a probabilistic measure, allowing for a more sensitive detection of subtle changes in representation quality. Tracking these metrics during training facilitates informed decisions regarding regularization strength and model architecture, ultimately leading to more robust and generalizable models.

Restoring Divergence: Angular Dispersion as a Countermeasure

Angular Dispersion functions as a regularization technique within neural networks by explicitly encouraging diversity in vector representations. This is achieved by adding a penalty to the loss function proportional to the cosine similarity between vectors representing different data points. Maximizing the angular distance – or minimizing the cosine similarity – between these vectors prevents the model from collapsing into a state where distinct inputs are mapped to nearly identical representations. The method operates on the principle that a robust model should exhibit a wide range of distinct outputs for a diverse set of inputs, and thus avoids overfitting to specific features or patterns within the training data. Effectively, Angular Dispersion promotes a more expressive and generalized internal representation of the input space.

Sliced Dispersion addresses the computational expense of calculating angular dispersion across all vector pairs in large-scale models by projecting vectors onto random unit vectors before computing cosine similarity. This technique reduces the complexity from $O(N^2D)$ to $O(ND)$ , where N is the number of vectors and D is the dimensionality of the vectors. By approximating the full angular dispersion with a sliced version, the regularization process remains computationally feasible for models with millions or billions of parameters, enabling practical application to neural machine translation (NMT) and other large-scale representation learning tasks without significant performance degradation.

Integration of Angular Dispersion as a regularization technique during Neural Machine Translation (NMT) model training has been shown to yield statistically significant improvements in model robustness against representation collapse. Empirical evaluation demonstrates a measurable increase in BLEU score, with p-values less than 0.05 indicating the observed performance gains are unlikely due to random chance. This improvement suggests that incorporating angular dispersion encourages the development of more diverse and reliable vector representations, directly mitigating the tendency of NMT models to converge on limited or degenerate solution spaces.

Representation collapse in neural machine translation (NMT) stems from the model’s tendency to map distinct input sequences to similar or identical vector representations, diminishing expressive capacity. Angular Dispersion directly counters this by explicitly maximizing the angular distance between these vectors during training. This regularization technique forces the model to develop more diverse and distinguishable representations, preventing the consolidation of information into a limited subspace. Consequently, the model is better equipped to capture nuanced differences in input, leading to more accurate and reliable translations, and mitigating the adverse effects of collapsed representations on downstream performance.

The Shadow of Compression: Post-Training Quantization and its Repercussions

Post-Training Quantization, or PTQ, represents a vital optimization strategy in the realm of deploying large language models. This technique dramatically reduces a model’s memory footprint and accelerates inference speeds, making it practical for resource-constrained environments and real-time applications. Rather than retraining a model, PTQ converts the model’s weights from high-precision floating-point numbers to lower-precision integer representations – typically 8-bit integers – with minimal performance loss. This compression not only shrinks the model size, easing storage and transmission burdens, but also leverages the computational efficiencies of integer arithmetic on modern hardware. Consequently, PTQ is often a first step in preparing sophisticated models for widespread use, enabling deployment on edge devices and facilitating faster, more responsive applications.

Post-training quantization, despite its advantages in reducing model size and accelerating inference, carries the risk of exacerbating representation collapse – a phenomenon where distinct tokens or concepts within a model’s embedding space converge onto similar representations. This collapse diminishes the model’s ability to differentiate between inputs, leading to a noticeable decline in performance, particularly on less frequent or nuanced data. Essentially, the aggressive compression inherent in quantization can strip away subtle but crucial distinctions learned during training, causing the model to generalize poorly and potentially output nonsensical or inaccurate predictions. The effect is not merely a uniform decrease in accuracy; rather, it disproportionately impacts the model’s ability to handle rare tokens or complex outputs, sometimes resulting in a complete loss of predictive power in these critical areas.

The CTranslate2 framework distinguishes itself through a highly optimized implementation of Post-Training Quantization (PTQ), enabling developers to substantially reduce model size and accelerate inference speeds without necessarily sacrificing accuracy. This isn’t merely a straightforward compression tool; it offers controlled compression, providing granular settings to fine-tune the quantization process. By allowing precise control over bit-widths and quantization schemes, CTranslate2 minimizes performance degradation often associated with aggressive compression. The framework’s efficiency stems from its focus on direct execution of quantized models, bypassing much of the overhead typical in other quantization workflows. This results in a practical solution for deploying large language models on resource-constrained devices, making advanced natural language processing more accessible and scalable.

Angular Dispersion regularization offers a compelling solution to the performance degradation often accompanying post-training quantization. This technique actively combats representation collapse, a phenomenon where model embeddings lose their distinctiveness, particularly impacting the accurate processing of rare tokens – instances where conventional, quantized models frequently yield scores near zero. Studies demonstrate that incorporating Angular Dispersion not only preserves performance levels but can actually enhance the prediction of these challenging tokens. Notably, in continuous output neural machine translation models, the regularization has proven capable of entirely preventing collapse, achieving a measurable BLEU score – greater than zero – in scenarios where baseline quantized models would otherwise produce no meaningful output.

Beyond the Horizon: Charting a Course for Future NMT Systems

Continuous Output Neural Machine Translation (NMT) represents a departure from traditional discrete output models, aiming to generate text as a continuous stream rather than predicting individual tokens. This innovative approach, while offering potential benefits in fluency and expressiveness, introduces a significant challenge: representation collapse. The model can inadvertently learn to map diverse input sequences to a limited range of continuous representations, effectively losing the nuanced information contained within the source text. This occurs because the absence of a clear separation between output tokens – a characteristic of discrete NMT – makes it difficult for the model to maintain distinct representations for different meanings. Consequently, the generated output can become repetitive, generic, or fail to accurately reflect the input, highlighting the need for robust regularization techniques and carefully designed training objectives to prevent this form of representational degradation.

Neural machine translation systems often encounter words not present in their training data, a challenge traditionally addressed through techniques like subword segmentation. Byte Pair Encoding (BPE) offers a particularly effective solution by breaking down rare or unseen words into more frequent, smaller units – subwords. This decomposition allows the model to represent and translate even out-of-vocabulary terms by combining known subword components. Consequently, the integration of BPE not only expands the model’s effective vocabulary but also significantly improves its robustness and ability to generalize to novel linguistic input, reducing the likelihood of encountering untranslatable tokens and enhancing overall translation quality.

The advancement of Neural Machine Translation (NMT) is increasingly reliant on robust and scalable training frameworks, and Fairseq has emerged as a pivotal tool in this regard. This open-source toolkit, developed by Meta AI, streamlines the process of building and deploying sophisticated Transformer models-the current state-of-the-art in NMT. Fairseq’s modular design allows researchers to readily experiment with different model architectures, training paradigms, and optimization strategies, accelerating innovation in the field. By providing pre-built components and efficient implementations of key algorithms, it lowers the barrier to entry for complex NMT research, enabling the development of models with increased capacity and improved performance on challenging translation tasks. The framework’s emphasis on reproducibility and scalability further solidifies its role in pushing the boundaries of what’s possible with NMT, fostering a collaborative environment for continued progress.

The future of Neural Machine Translation (NMT) hinges on refining existing techniques and pioneering new ones, with ongoing research concentrating on three key areas. Regularization methods are being explored to prevent overfitting and enhance generalization, while model compression strategies aim to reduce computational costs and enable deployment on resource-constrained devices. Simultaneously, investigations into novel architectures-beyond the standard Transformer-promise to overcome current limitations and unlock improved performance. Progress in these areas is not merely theoretical; it’s demonstrably reflected in metrics such as $Rényi Entropy$ and $Spherical Variance$ , which quantify the diversity and quality of generated translations, offering a tangible measure of advancements and indicating the potential for truly nuanced and accurate machine translation systems.

The pursuit of increasingly efficient machine translation models, as detailed in the study of representation collapse, inevitably introduces simplification. This simplification, while offering immediate gains in computational cost or model size, carries a future cost. Donald Davies observed, “There is no possibility of producing a computer which can think.” This resonates with the findings; the very act of quantizing or moving towards continuous-output models, while seemingly streamlining the process, risks collapsing the rich, angular dispersion of representations crucial for nuanced translation. The research suggests that maintaining this dispersion-actively resisting complete simplification-is not merely a technical detail, but a necessary condition for graceful aging of the system, ensuring it doesn’t prematurely lose its ability to accurately convey meaning.

What Lies Ahead?

The pursuit of representational diversity, as demonstrated by this work, isn’t merely a quest for better translation; it’s an acknowledgement of the inherent entropy within any complex system. Versioning, in this context, becomes a form of memory – a continuous attempt to capture and preserve signal against the backdrop of decay. The observed benefits of promoting angular dispersion suggest that the collapse phenomenon isn’t a bug, but a feature – a natural tendency toward homogeneity given the pressures of optimization.

Future work will inevitably grapple with the limits of this approach. Can dispersion be generalized beyond the decoder, or even beyond neural machine translation? The arrow of time always points toward refactoring; as models grow in scale and complexity, the mechanisms that prevent representational collapse will themselves become targets for further refinement. The current focus on variance and angular separation feels like a first step – a mapping of the terrain before the real engineering begins.

Ultimately, the question isn’t whether representation collapse can be eliminated, but whether it can be managed, channeled, or even exploited. A truly graceful aging process for these systems will require a deeper understanding of the interplay between optimization, generalization, and the inevitable pull toward uniformity.

Original article: https://arxiv.org/pdf/2602.17287.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/