Synthetic and Natural Noise Both Break Neural Machine Translation (1711.02173v2)

Abstract: Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.

• The paper demonstrates that NMT models are highly sensitive to both synthetic and natural noise, leading to significant reductions in BLEU scores.
• The paper employs character-based and word-level models (charCNN, char2char, and Nematus) to systematically assess the impact of different noise types on translation quality.
• The paper shows that adversarial training and structure-invariant representations can partially mitigate noise effects, enhancing model robustness.

Synthetic and Natural Noise Both Break Neural Machine Translation

The paper "Synthetic and Natural Noise Both Break Neural Machine Translation" by Yonatan Belinkov and Yonatan Bisk rigorously investigates the susceptibility of neural machine translation (NMT) models to various types of noise. Their research highlights that although character-based NMT models ostensibly mitigate out-of-vocabulary (OOV) issues and enhance morphological learning, they remain exceedingly brittle when exposed to noisy inputs.

Key Findings

The primary contribution of this work is a systematic examination of the fragility of NMT models under different noise conditions. The authors focus on three noise types:

1. Synthetic Noise: This includes random permutation, character swapping, and keyboard typos.
2. Natural Noise: This encapsulates realistic errors such as those found in human-typed texts, including omissions and phonetic mistakes.
3. Mixed Noise: A combination of the above noise types.

The authors employ three NMT models:

1. charCNN: A character convolutional neural network.
2. char2char: A fully character-based sequence-to-sequence model.
3. Nematus: A word-level model employing byte-pair encoding (BPE).

Their experiments reveal that even state-of-the-art models like char2char and Nematus degrade significantly under noisy conditions. For instance, char2char and Nematus exhibit dramatic BLEU score reductions when translating German texts with swapped or randomly permuted characters.

Robustness Strategies

The authors explore two strategies to enhance robustness:

1. Structure-Invariant Representations: The meanChar model, which averages character embeddings to create word representations, reduces the sensitivity to character order. However, while it moderately handles scrambled texts, it performs poorly under key-tampering or natural noise.
2. Adversarial Training: Training models on noisy data can substantially improve their performance on noisy texts. The charCNN models trained on mixed noise types showed notable robustness across various noise types. This ensemble approach yields models that, while not optimal for any single noise type, demonstrate improved generalization across diverse noisy inputs.

For example, a charCNN model trained on a mix of random noise, keyboard typos, and natural errors achieved a robust generalization, evidencing comparatively high BLEU scores across all noisy conditions.

Analysis of Model Weights

An intriguing aspect of this paper is the analysis of convolutional filter weights in charCNN models. The authors show that models trained on random noise exhibit low variance in their filter weights, suggesting a learned robustness akin to mean operations over character embeddings. In contrast, high variances in models trained on natural noise indicate a more complex learning pattern required to handle realistic errors.

Implications

This research has considerable implications for the deployment of NMT systems, particularly in real-world scenarios where texts are rarely pristine. It underscores the necessity for models to be robust to both synthetic and natural noise to ensure reliability and usability.

Practically, this means developing NMT architectures capable of generalizing well without extensive noise-specific training data. Theoretically, it calls for an enhanced understanding of human error patterns and possibly integrating phonetic and syntactic structures into noise generation models.

Future Directions

The findings of this work pave the way for future research in several directions:

1. Improved Noise Modeling: Developing more sophisticated models to generate realistic noise, leveraging linguistic properties such as phonetics and syntax.
2. Architectural Innovations: Designing NMT models that inherently possess noise robustness without the need for specific noisy training datasets.
3. Cross-Linguistic Studies: Extending this research to a broader range of languages and error types to understand universal versus language-specific challenges in NMT robustness.

In conclusion, while the paper emphatically demonstrates the challenges posed by noisy data to NMT systems, it also provides viable paths forward in making these systems more robust and reliable.