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Advancing Explainability in Neural Machine Translation: Analytical Metrics for Attention and Alignment Consistency

Anurag Mishra

TL;DR

This work addresses the opacity of Neural Machine Translation (NMT) by proposing a quantitative framework that links internal attention patterns to external statistical alignments and standard quality metrics. It introduces attention entropy $H_t$ and alignment agreement, measured against FasterAlign-style references, and analyzes their relationships with BLEU and METEOR using a pre-trained $mT5$ on the WMT14 English–German corpus. The results show that sharper, more focused attention (lower $H_t$) tends to align better with external references and yields modest improvements in METEOR, while higher translation quality is not guaranteed solely by interpretability. Overall, the approach provides a data-driven method to validate and enhance NMT explainability, guiding future work toward more transparent and reliable MT systems.

Abstract

Neural Machine Translation (NMT) models have shown remarkable performance but remain largely opaque in their decision making processes. The interpretability of these models, especially their internal attention mechanisms, is critical for building trust and verifying that these systems behave as intended. In this work, we introduce a systematic framework to quantitatively evaluate the explainability of an NMT model attention patterns by comparing them against statistical alignments and correlating them with standard machine translation quality metrics. We present a set of metrics attention entropy and alignment agreement and validate them on an English-German test subset from WMT14 using a pre trained mT5 model. Our results indicate that sharper attention distributions correlate with improved interpretability but do not always guarantee better translation quality. These findings advance our understanding of NMT explainability and guide future efforts toward building more transparent and reliable machine translation systems.

Advancing Explainability in Neural Machine Translation: Analytical Metrics for Attention and Alignment Consistency

TL;DR

This work addresses the opacity of Neural Machine Translation (NMT) by proposing a quantitative framework that links internal attention patterns to external statistical alignments and standard quality metrics. It introduces attention entropy and alignment agreement, measured against FasterAlign-style references, and analyzes their relationships with BLEU and METEOR using a pre-trained on the WMT14 English–German corpus. The results show that sharper, more focused attention (lower ) tends to align better with external references and yields modest improvements in METEOR, while higher translation quality is not guaranteed solely by interpretability. Overall, the approach provides a data-driven method to validate and enhance NMT explainability, guiding future work toward more transparent and reliable MT systems.

Abstract

Neural Machine Translation (NMT) models have shown remarkable performance but remain largely opaque in their decision making processes. The interpretability of these models, especially their internal attention mechanisms, is critical for building trust and verifying that these systems behave as intended. In this work, we introduce a systematic framework to quantitatively evaluate the explainability of an NMT model attention patterns by comparing them against statistical alignments and correlating them with standard machine translation quality metrics. We present a set of metrics attention entropy and alignment agreement and validate them on an English-German test subset from WMT14 using a pre trained mT5 model. Our results indicate that sharper attention distributions correlate with improved interpretability but do not always guarantee better translation quality. These findings advance our understanding of NMT explainability and guide future efforts toward building more transparent and reliable machine translation systems.

Paper Structure

This paper contains 11 sections, 7 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Explainability Component
  4. Methodology
  5. Model and Data Setup
  6. Attention Extraction and Entropy
  7. Statistical Alignments and Agreement Metric
  8. Translation Quality Metrics
  9. Correlation Analysis
  10. Results
  11. Conclusion

Figures (3)

  • Figure 1: Attention heatmaps for raw, row-normalized, column-normalized, and softmax-normalized matrices. Larger models focus attention more effectively.
  • Figure 2: Correlation between attention entropy and alignment agreement. Lower entropy correlates with better alignment.
  • Figure 3: Comparison of attention entropy and METEOR scores. Lower entropy tends to correspond with slightly higher METEOR scores.