Robust Neural Machine Translation with Doubly Adversarial Inputs (1906.02443v1)

Abstract: Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input. We propose an approach to improving the robustness of NMT models, which consists of two parts: (1) attack the translation model with adversarial source examples; (2) defend the translation model with adversarial target inputs to improve its robustness against the adversarial source inputs.For the generation of adversarial inputs, we propose a gradient-based method to craft adversarial examples informed by the translation loss over the clean inputs.Experimental results on Chinese-English and English-German translation tasks demonstrate that our approach achieves significant improvements ($2.8$ and $1.6$ BLEU points) over Transformer on standard clean benchmarks as well as exhibiting higher robustness on noisy data.

An Analysis of "Robust Neural Machine Translation with Doubly Adversarial Inputs"

The paper "Robust Neural Machine Translation with Doubly Adversarial Inputs" presents a novel approach to enhancing the robustness of Neural Machine Translation (NMT) systems using doubly adversarial inputs. This research addresses the well-known vulnerability of NMT models to noisy perturbations in input sentences, a critical aspect that can significantly degrade translation quality. The authors propose a dual strategy comprising both attacking the translation model with adversarial source inputs and defending it using adversarial target inputs.

Methodological Overview

The paper proposes a gradient-based method named $AdvGen$ to generate adversarial examples. This approach constructs perturbations informed by the translation loss computed on clean inputs. The method is employed during both encoding and decoding stages, aiming to simulate conditions that robustly challenge and therefore strengthen the NMT models. Unlike prior works which typically focused on either black-box or context-free perturbations, this paper leverages white-box attacks by accessing model parameters to tailor adversarial inputs specifically against the model.

Results and Experimental Validation

Significant improvements have been demonstrated in translation accuracy and robustness using the proposed method. On Chinese-English and English-German translation tasks, it shows measurable gains of 2.8 BLEU points and 1.6 BLEU points over standard Transformer models, respectively. Additionally, the results indicate enhanced performance on datasets with artificial noise, further confirming the approach's efficacy. The paper carefully evaluates different elements of the approach, including the impact of adversarial inputs on both source and target sides, providing insights into which components contribute most significantly to the observed performance gains.

Theoretical and Practical Implications

The theoretical implication of this paper lies in demonstrating that targeted adversarial training can mitigate input noise vulnerabilities effectively in NMT systems. Practically, it offers a systematic approach to refine NMT models, facilitating more reliable and consistent machine translation outputs under diverse real-world noise conditions. The use of adversarial inputs as both an attacking and a defensive mechanism is particularly insightful, providing a pathway for future research to explore adversarial training across various sequence-to-sequence tasks beyond machine translation.

Future Directions

This paper sets a precedent for further exploration in adversarial training within machine translation and other language tasks. Future work might extend to developing techniques that generate more naturally coherent adversarial examples, moving beyond simplistic word-level perturbations. Combining this approach with curriculum learning strategies could offer novel avenues for enhancing model robustness progressively.

In conclusion, this research notably contributes to advancing the robustness of NMT applications, delivering both theoretical contributions to adversarial training methodologies and practical solutions for improving translation quality in noisy environments.