SentencePiece: A Powerful Subword Tokenization Algorithm

SentencePiece is a subword tokenization library developed by Google that addresses open vocabulary issues in neural machine translation (NMT).

SentencePiece is a data-driven unsupervised text tokenizer. Unlike traditional tokenizers that rely on pre-tokenization (e.g., whitespace splitting), SentencePiece treats the input text as a raw sequence of Unicode characters, enabling it to handle diverse languages and special characters effectively.

It decomposes text into subword units instead of words or characters, thereby alleviating problems related to OOV tokens while still capturing meaningful linguistic structures.

Key Features of SentencePiece

SentencePiece is a reimplementation of subword units that offers a more robust and language-agnostic approach to text tokenization compared to traditional methods. It supports two key segmentation algorithms: byte-pair encoding (BPE) and the unigram language model.

It distinguishes itself from other implementations in several key ways:

Predetermined Vocabulary Size: Unlike many unsupervised word segmentation algorithms that assume an infinite vocabulary, SentencePiece trains its segmentation model to achieve a fixed vocabulary size (e.g., 8k, 16k, 32k). This is a crucial difference from tools like subword-nmt, which uses the number of merge operations (a BPE-specific parameter) instead of a direct vocabulary size target. This allows SentencePiece to be more flexible and applicable to various segmentation algorithms beyond just BPE.

Training from Raw Sentences: Previously, subword implementations required pre-tokenized input, necessitating language-specific tokenizers and complicating preprocessing. SentencePiece is efficient enough to train directly from raw sentences, eliminating this dependency. This is particularly beneficial for languages like Chinese and Japanese, which lack explicit word delimiters.

Whitespace as a Basic Symbol: A key innovation of SentencePiece is its handling of whitespace. Traditional tokenizers often lose information during tokenization, making reversible conversion impossible. For example, tokenizing “World.” and “World .” can result in the same token sequence, losing the information about the space. SentencePiece treats whitespace as a regular symbol, escaping it with a meta-symbol “__” (U+2581). This feature ensures reversible tokenization without relying on language-specific resources.

Subword Regularization and BPE-Dropout: SentencePiece supports subword regularization and BPE-dropout, which are regularization techniques that augment training data through on-the-fly subword sampling. This improves the accuracy and robustness of NMT models. To utilize this, the SentencePiece library (C++/Python) can be integrated into the NMT system to sample different segmentations for each parameter update. This dynamic sampling contrasts with standard off-line data preparation.

In summary, SentencePiece offers a robust and versatile approach to subword tokenization, addressing key limitations of previous methods. Its ability to handle raw text, enforce a fixed vocabulary size, explicitly manage whitespace, and support regularization techniques makes it a valuable tool for modern NLP, especially in the context of neural machine translation.

Implementation

The actual implementation of SentencePiece can be done using pre-built libraries that facilitate integration into larger NLP pipelines. The SentencePiece library, available in C++ and Python, offers a simple API for training and applying models.

# Installation
pip install sentencepiece

Training Example

To train a SentencePiece model, one would utilize the sentencepiece_model training method. Here’s a sample code snippet in Python:

import sentencepiece as spm

# Training SentencePiece model
spm.SentencePieceTrainer.Train('--input=data.txt --model_prefix=m --vocab_size=32000')

This command reads from data.txt, generating a model file (m.model) and a vocabulary file (m.vocab of size 32000) based on the training data.

Tokenization Example

Once the model is trained, it can be used for tokenizing text:

# Loading the model
sp = spm.SentencePieceProcessor(model_file='m.model')

# Encode and decode example
encoded = sp.encode('This is a test')
decoded = sp.decode(encoded)

tokens = sp.encode('This is a test', out_type=str)

print("Encoded:", encoded)  # Output: [284, 47, 11, 4, 15, 400]
print("Decoded:", decoded)  # Output: "This is a test"
print("Tokens:", tokens)    # Output: ['▁This', '▁is', '▁a', '▁', 't', 'est']