What are the advantages and disadvantages of using byte-pair encoding (BPE) for tokenization in neural machine translation?

Byte-pair encoding (BPE) is a subword tokenization algorithm commonly used in neural machine translation to address the limitations of word-based and character-based tokenization. It offers several advantages and disadvantages. The main advantage of BPE is its ability to handle out-of-vocabulary (OOV) words. Unlike word-based tokenization, which assigns a unique token to each word in the vocabulary, BPE breaks down words into smaller subword units, which are then used as the vocabulary. This allows the model to represent rare words as combinations of more frequent subwords, reducing the number of OOV words and improving translation quality. For example, the word "unbelievable" might be broken down into "un", "believe", and "able". BPE also allows the model to capture morphological information. By learning representations for common prefixes, suffixes, and roots, the model can better understand the meaning and structure of words. This can be particularly beneficial for languages with rich morphology. Another advantage of BPE is that it allows for a more compact vocabulary compared to character-level models, while still being able to handle OOV words, leading to a good trade-off between vocabulary size and model performance. However, BPE also has some disadvantages. One disadvantage is that it can sometimes break down words into unnatural subword units, which can make it more difficult for the model to learn meaningful representations. The subword units may not always correspond to linguistically meaningful morphemes, leading to less interpretable representations. BPE's greedy merging process can also lead to suboptimal tokenizations. The initial merges are based on frequency, and subsequent merges may be influenced by these early decisions, leading to less effective subword segmentation. Furthermore, while BPE reduces OOV words, it does not eliminate them entirely. There may still be rare subword units that are not present in the vocabulary, which will be treated as UNK tokens. Despite these disadvantages, BPE is a widely used and effective tokenization algorithm for neural machine translation, offering a good balance between handling OOV words, capturing morphological information, and maintaining a reasonable vocabulary size.