Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation (2010.10907v3)

Abstract: In Neural Machine Translation (and, more generally, conditional LLMing), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence. While many attempts to understand the internal workings of NMT models have been made, none of them explicitly evaluates relative source and target contributions to a generation decision. We argue that this relative contribution can be evaluated by adopting a variant of Layerwise Relevance Propagation (LRP). Its underlying 'conservation principle' makes relevance propagation unique: differently from other methods, it evaluates not an abstract quantity reflecting token importance, but the proportion of each token's influence. We extend LRP to the Transformer and conduct an analysis of NMT models which explicitly evaluates the source and target relative contributions to the generation process. We analyze changes in these contributions when conditioning on different types of prefixes, when varying the training objective or the amount of training data, and during the training process. We find that models trained with more data tend to rely on source information more and to have more sharp token contributions; the training process is non-monotonic with several stages of different nature.

• The paper extends Layer-wise Relevance Propagation to measure source and target token contributions in transformer-based neural machine translation.
• Results show that initial translations rely on source context, while longer target sequences increasingly leverage target prefix cues.
• Findings indicate that larger training datasets and bias mitigation techniques enhance translation fidelity and model interpretability.

Analyzing Contributions in Neural Machine Translation: A Methodological Overview

The paper "Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation" presents a meticulous investigation of the relative contributions of source and target contexts in conditional LLMs, specifically Neural Machine Translation (NMT). This inquiry is conducted through the lens of Layer-wise Relevance Propagation (LRP), a nuanced interpretability framework traditionally used in computer vision. The authors, Voita, Sennrich, and Titov, propose a variant of LRP adapted for transformer-based NMT models, allowing for a precise decomposition of prediction influence attributable to source and target tokens.

Core Contributions

The authors tackle the challenge of quantifying the contributions of source texts and target prefixes in the generation of translations by extending LRP to operate within the architecture of transformers. This is a significant stride from conventional methods that often fail to distinguish clearly between the contributions of these two contexts. The relevance propagation in LRP operates under a conservation principle, ensuring a fixed contribution sum from input to prediction, thereby providing a nuanced understanding of prediction dynamics beyond mere token importance.

Key Findings

1. Source and Target Contributions: The paper reveals that as the target sequence lengthens, the model's dependence on the source diminishes—evidently replaced by reliance on contextual coherence offered by the target prefix. Yet, at the initial translation stages, the source text heavily steers the predictions.
2. Effect of Training Data Size: Models trained with more extensive datasets exhibit increased reliance on source context and demonstrate more discriminate token importance, implying that data abundance facilitates stronger alignment between source reliance and targeted token decisiveness.
3. Influence of Training Phases: The investigation of training dynamics unveils non-linear phases wherein contribution patterns oscillate. The authors identify distinct stages, each characterized by variation in source influence and entropy, reflecting a learning path that traverses through diverse reliance strategies before converging.
4. Mitigation of Exposure Bias: The paper underscores that minimizing exposure bias via techniques like Minimum Risk Training (MRT) enhances source reliance, reducing the propensity for hallucinations—a scenario where the model generates fluent but inadequately grounded translations.
5. Behaviour with Varied Prefixes: By analyzing models fed with different types of target prefixes, the authors discern a nuanced response wherein model-generated prefixes stimulate greater source reliance compared to references or random sentences, the latter potentially triggering hallucination-like tendencies.

Theoretical and Practical Implications

The paper’s insights have profound implications for model interpretability in machine learning, particularly enhancing understanding of transformer-based NMT systems. Practically, the findings inform the design of training regimes that mitigate undesirable biases by reinforcing source context utility, thereby potentially improving translation fidelity.

Prospective Developments

Future explorations may capitalize on this methodological foundation to probe further into model robustness, especially when tasked with complex, non-monotonic language alignments or when adapting across varied linguistic domains. Additionally, extending this framework could aid in refining predictive behaviors not only in translation tasks but across the breadth of sequence generation applications in natural language processing.

The methodological innovations and analytical insights this paper presents contribute richly to the scholarship on NMT, emphasizing the importance of understanding internal workings to bolster both model transparency and efficacy.