Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (1804.10959v1)

Abstract: Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram LLM. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.

Subword Regularization: Enhancing Neural Machine Translation Models

The paper "Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates" by Taku Kudo addresses a fundamental issue in Neural Machine Translation (NMT): the open vocabulary problem. This issue arises due to fixed word vocabularies, leading to inaccurate translations when encountering unknown words. This paper proposes subword regularization, a novel technique that improves model robustness and translation accuracy by leveraging the ambiguity inherent in subword segmentation.

Introduction and Context

Subword units have become a popular approach to mitigate open vocabulary problems in NMT. Traditionally, methods like Byte-Pair-Encoding (BPE) have been employed to segment words into subunits, thereby reducing the vocabulary size and handling rare words more effectively. However, subword segmentation can be ambiguous, with multiple potential segmentations for the same sentence. Kudo's work explores the potential of using this segmentation ambiguity as a source of noise to regularize and improve NMT models.

Core Contributions

The paper introduces two main contributions:

1. Subword Regularization Technique: A probabilistic approach to train NMT models using multiple subword segmentations. By sampling different segmentations on-the-fly during training, the method introduces variability and robustness against segmentation errors without altering the NMT architecture.
2. Unigram LLM for Subword Segmentation: An alternative to BPE, this model generates multiple plausible segmentations with associated probabilities, improving the sampling process' realism and efficacy.

Methodology

NMT Training with On-the-Fly Subword Sampling

The central idea involves treating subword segmentation as a probabilistic process. Given source and target sentences, multiple subword segmentations are considered during training, optimizing the model's parameters with a marginalized likelihood that accounts for these variations. The practical implementation approximates this by sampling segmentation candidates on-the-fly, ensuring the model learns to handle a range of possible segmentations, thereby reducing overfitting.

Decoding Techniques

For decoding, the model typically translates the best segmentation candidate based on probability. The paper also explores using $n$-best segmentations to enhance translation by selecting the best-scoring translation among multiple candidates, further leveraging segmentation variability.

Experimental Evaluation

Empirical evaluations demonstrate the effectiveness of subword regularization across various datasets, languages, and resource settings. Notably, the method shows significant improvements in low-resource and out-of-domain settings, suggesting its robustness and generalizability. Detailed results (Table * in the paper) quantify these improvements, with BLEU score enhancements ranging from 1 to 2 points over baseline methods.

Comparative Analysis

The paper contrasts subword regularization with other segmentation algorithms, such as pure word, character, and mixed word/character models. The unigram LLM with subword regularization consistently outperforms these baselines, illustrating its superior handling of segmentation and noise (see Table * for specific results).

Implications and Future Directions

The contributions of this paper have notable implications for both practical NMT systems and theoretical advancements in handling textual ambiguity. The introduction of probabilistic subword segmentations pushes forward the understanding of how variability can be harnessed to improve machine learning models.

Future avenues for this work include extending subword regularization to other encoder-decoder tasks like dialogue generation and summarization, where data scarcity might lead to significant gains from this approach. Additionally, integrating subword regularization with other robust training techniques such as Denoising Auto Encoders (DAEs) or Adversarial Training could further amplify its benefits.

Conclusion

The novel subword regularization technique and the accompanying unigram LLM proposed in this paper represent a meaningful step in improving the robustness and accuracy of NMT models. The method's effectiveness, especially in low-resource and out-of-domain scenarios, underscores its potential to enhance various NLP applications.

Implementations, as referenced, are publicly available, encouraging ongoing explorations and refinements within the community. This openness fosters reproducibility and further validation, paving the way for broader adoption and potential extensions of this innovative approach.

References

The document cites seminal works and recent advancements in NMT and subword segmentation methods, providing a comprehensive context for the contributions presented. Notable references include foundational papers on NMT architectures and BPE, situating the current work within the continuum of machine translation research.