What is WordPiece?
WordPiece is an algorithm used in natural language processing to break down words into smaller, more manageable subwords. This subword segmentation method is a type of unsupervised learning, which means that it does not require human annotation or pre-defined rules to work.
The WordPiece algorithm starts by initializing a word unit inventory with all the characters in the language. A language model is then built using this inventory, which allows the algorithm to identify the most frequent combinations of symbols in the vocabulary. These frequent combinations are added to the vocabulary through an iterative process that chooses the new word unit that increases the likelihood of the training data the most when added to the model.
The result of this process is a set of subwords that can be used to represent words in the language. By breaking down words into smaller, more manageable subwords, the WordPiece algorithm can effectively deal with rare words and other challenging language problems in natural language processing.
How Does WordPiece Work?
The WordPiece algorithm works in four main steps:
- Initialization: The word unit inventory is initialized with all the characters in the text. This includes letters, numbers, and special characters.
- Training: A language model is built on the training data using the inventory from step 1. The language model allows the algorithm to identify the most frequent combinations of symbols in the vocabulary.
- Subword Generation: The algorithm generates a new subword by combining two units out of the current word inventory to increment the word unit inventory by one. The new subword is chosen out of all the possible ones that increase the likelihood on the training data the most when added to the model.
- Iteration: The process from step 2 onwards is repeated until a predefined limit of subwords is reached or the likelihood increase falls below a certain threshold. This ensures that the algorithm only generates relevant subwords that are useful for representing words in the language.
Why is WordPiece Useful?
WordPiece is useful for a variety of language processing tasks because it can effectively deal with rare words and other common challenges in natural language processing.
Rare words are a common problem in natural language processing because they are infrequent and difficult to model. By breaking down words into smaller subwords, the WordPiece algorithm can better represent rare words and allow the language model to generate more accurate predictions.
Another challenge in natural language processing is dealing with out-of-vocabulary (OOV) words. OOV words are words that are not in the training data and are difficult for the language model to handle. By breaking down words into subwords, the WordPiece algorithm can effectively capture the meaning of OOV words by representing them as a combination of known subwords.
Overall, the WordPiece algorithm is a powerful tool for natural language processing that can help researchers and developers tackle many of the common challenges in this field.
