Unigram Tokenization

Summary

This video explains the Unigram Language Model (LM) subword tokenization algorithm. It demonstrates how a Unigram LM tokenizer starts with a large vocabulary and iteratively reduces it by removing tokens that minimally impact the loss on a training corpus. The algorithm utilizes the Expectation-Maximization method to estimate token probabilities and remove the least impactful tokens, keeping basic characters for efficient tokenization. Through an example corpus, the video shows the process of tokenizing words, calculating loss, and iterating until the desired vocabulary size is achieved. Efficient computation is achieved using the Viterbi algorithm for tokenization.

Takeaways

• 😀 The Unigram Language Model (LM) tokenization algorithm begins with a large vocabulary and reduces it over iterations.
• 😀 The goal is to remove tokens that increase the loss the least in order to reduce the vocabulary size.
• 😀 The Unigram LM assumes that the occurrence of each word is independent of its previous word, which simplifies probability calculations.
• 😀 Unigram models are useful for tokenization, as they help estimate the relative likelihood of different tokenized sequences.
• 😀 The Expectation-Maximization method is used in training the Unigram tokenizer to iteratively estimate probabilities and remove tokens.
• 😀 The training process involves tokenizing words in the corpus and calculating the loss, which is the sum of word frequencies multiplied by the negative log of tokenization probabilities.
• 😀 Tokenizations with the highest probabilities are chosen during the training process, and the model removes tokens that minimally impact the loss.
• 😀 The Viterbi algorithm is used in practice for efficient tokenization by calculating the most likely tokenizations without considering all possible segmentations.
• 😀 The vocabulary is reduced by removing tokens that have minimal impact on the loss, ensuring that basic characters remain in the vocabulary.
• 😀 The algorithm repeats iterations to gradually reduce the vocabulary size until the desired vocabulary size is reached.
• 😀 The simplicity of the Unigram LM makes it a valuable tool for tokenization tasks, although it would not be ideal for generating diverse text sequences.

Q & A

What is the primary purpose of the Unigram Language Model (LM) in tokenization?

The Unigram Language Model (LM) is used in tokenization to estimate the relative likelihood of different tokenizations of a word by assigning probabilities to sequences of tokens, allowing for the selection of the most probable segmentation.

How does the Unigram LM training algorithm start its process?

The Unigram LM training algorithm begins with a very large vocabulary and then iteratively removes tokens until the desired vocabulary size is reached. The decision to remove tokens is based on a loss calculation after each iteration.

What is the role of the loss calculation in the Unigram LM training algorithm?

The loss calculation is used to evaluate how the removal of a token affects the model's ability to represent the corpus. Tokens that minimally increase the loss are more likely to be removed, helping refine the vocabulary.

What does the Unigram LM assume about the relationship between tokens in a sequence?

The Unigram LM assumes that each token's occurrence is independent of the previous tokens, which simplifies the calculation of the probability of a text as the product of individual token probabilities.

Why is the Unigram LM considered simple, and what are its limitations?

The Unigram LM is considered simple because it assumes that tokens are independent, which leads to consistent probabilities across the sequence. However, this simplicity limits its ability to generate coherent text, as it doesn't account for context or the relationship between tokens.

What is the Expectation-Maximization method used for in the Unigram LM training process?

The Expectation-Maximization (EM) method is used to iteratively estimate the probabilities of tokens and update the vocabulary by removing the least impactful tokens based on loss calculation.

How are probabilities of tokens calculated in the Unigram LM?

The probability of a token is calculated as the frequency of the token's appearance in the training corpus divided by the total number of appearances of all tokens in the corpus.

What happens if we remove a token from the vocabulary during the training process?

Removing a token from the vocabulary may change the tokenization of words, but if the removed token had little effect on the overall loss, the process of tokenization may remain the same for other words, and the loss might not increase significantly.

How does the algorithm decide which token to remove during each iteration?

The algorithm calculates the loss for each possible token removal and removes the token that impacts the loss the least. This process is repeated until the desired vocabulary size is achieved.

What is the Viterbi algorithm's role in the Unigram LM tokenization?

The Viterbi algorithm is used for efficient tokenization in the Unigram LM, as it allows for faster calculation of the best tokenization for a word, compared to the more brute-force approach of evaluating all possible segmentations.