BPE-Dropout: Simple and Effective Subword Regularization (1910.13267v2)

Abstract: Subword segmentation is widely used to address the open vocabulary problem in machine translation. The dominant approach to subword segmentation is Byte Pair Encoding (BPE), which keeps the most frequent words intact while splitting the rare ones into multiple tokens. While multiple segmentations are possible even with the same vocabulary, BPE splits words into unique sequences; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors. So far, the only way to overcome this BPE imperfection, its deterministic nature, was to create another subword segmentation algorithm (Kudo, 2018). In contrast, we show that BPE itself incorporates the ability to produce multiple segmentations of the same word. We introduce BPE-dropout - simple and effective subword regularization method based on and compatible with conventional BPE. It stochastically corrupts the segmentation procedure of BPE, which leads to producing multiple segmentations within the same fixed BPE framework. Using BPE-dropout during training and the standard BPE during inference improves translation quality up to 3 BLEU compared to BPE and up to 0.9 BLEU compared to the previous subword regularization.

An Evaluation of "BPE-Dropout: Simple and Effective Subword Regularization"

The paper "BPE-Dropout: Simple and Effective Subword Regularization" presents a novel method for enhancing the learning of neural machine translation (NMT) systems through subword regularization. The authors introduce BPE-dropout, a technique leveraging the existing Byte Pair Encoding (BPE) framework to yield multiple segmentations of words, addressing limitations in the deterministic nature of the conventional BPE segmentation.

Problem Statement

BPE is extensively used for subword segmentation in NMT as it efficiently handles the open vocabulary problem by maintaining frequent words intact while segmenting infrequent words. However, its deterministic nature restricts the model to learning from a singular segmentation per word, potentially hindering effective learning of word compositionality and robustness to segmentation errors.

Methodology

The authors propose BPE-dropout, a method compatible with traditional BPE. It stochastically drops some merge operations during training, allowing words to be segmented in multiple ways. This variability introduces a regularization effect that exposes models to diverse word compositions. During inference, standard BPE is used to maintain consistency.

Key contributions include demonstrating the superior performance of BPE-dropout against standard BPE and previous subword regularization strategies, particularly across a range of translation tasks. The paper also provides an analysis indicating that training with BPE-dropout enhances the quality of learned token embeddings and robustness against noisy input.

Experimental Setup and Results

The paper reports substantial improvements in BLEU scores on several datasets, with BPE-dropout outperforming standard BPE by up to 2.3 BLEU points and previous regularization methods by up to 0.9 BLEU points. These results are consistent across various language pairs and dataset sizes, highlighting the effectiveness of BPE-dropout, especially in low-resource settings.

For large datasets, the paper finds that applying BPE-dropout mostly on the source side of translation pairs yields optimal performance. This suggests a practical approach for balancing computational complexity and translation quality, wherein the model's understanding of input is prioritized.

Discussion and Implications

The introduction of BPE-dropout offers several theoretical and practical implications. By challenging the deterministic segmentation of BPE, this method encourages models to develop a richer understanding of language by exposing them to a broader set of linguistic structures during training. This approach not only enhances translation accuracy but also equips models to handle real-world input better, which often contains misspellings and other noise.

The authors demonstrate that BPE-dropout achieves robustness to input noise, achieving significant BLEU improvements when tested on corrupted input texts. This capability might be particularly advantageous in applications with noisy language data, such as social media or online content translation.

Future Directions

Future research may focus on refining BPE-dropout by adapting dropout rates dynamically, possibly through a learning mechanism that takes into account context or specific language attributes. Exploring BPE-dropout in conjunction with other segmentation algorithms like SentencePiece might provide further insights into optimizing subword units for diverse language processing tasks. Additionally, examining the potential of BPE-dropout for other NLP tasks outside of translation could expand its utility across the field.

Conclusion

In conclusion, the BPE-dropout method proposed in this paper represents a significant methodological improvement over traditional BPE segmentation by incorporating stochastic elements that enhance model learning. The empirical results underscore its effectiveness in improving model performance across metrics of translation accuracy and input robustness, suggesting broader applicability in various machine learning tasks involving language processing.