NLP: Understanding the N-gram language models

Summary

This video dives into core NLP tasks, starting with language models and progressing to models that handle sequences like part-of-speech tagging and named entity recognition. The speaker explains how language models estimate the probability of the next word in a sequence using toy corpora and n-grams. They address challenges like sequence normalization and the importance of 'fake tokens' to handle sequence termination. Practical applications in text generation, machine translation, and speech recognition are highlighted, showing how these models power technology like automatic email replies. The video lays a foundation for understanding how to train and evaluate these models.

Takeaways

• 😀 Language models estimate the probability of the next word given the previous words in a sequence.
• 😀 N-grams are sequences of 'n' words used to model language, and the choice of 'n' influences the model's performance.
• 😀 In a toy corpus, probability can be estimated by counting occurrences of word sequences like 'the house' or 'Jack built'.
• 😀 Bigrams (two-word sequences) can be used to calculate the probability of a word given its predecessor, e.g., the probability of 'check' given 'built'.
• 😀 Applications like email auto-replies, machine translation, and speech recognition rely on language models to generate text.
• 😀 The chain rule helps break down complex sequences into manageable probabilities by conditioning each word on the previous ones.
• 😀 Markov assumption simplifies the model by ignoring long histories and conditioning only on the last 'n' words.
• 😀 To handle the first word of a sequence (which can be unpredictable), a 'fake start' token is used to condition the first word's probability.
• 😀 Normalizing probabilities across different sequence lengths ensures the sum of probabilities equals 1, which makes the model properly calibrated.
• 😀 The addition of a 'fake end' token ensures proper termination of sequences and prevents errors in generating probabilities for incomplete sentences.
• 😀 The model's generative process allows it to evaluate different sequences and correctly allocate probability mass across them.
• 😀 A bigram language model factors probabilities into smaller terms (unigrams and bigrams) and can be trained and tested using real data.

Q & A

What is the main focus of Week 2 in the NLP course?

Week 2 focuses on core NLP tasks, particularly language models and sequence-based models like part-of-speech tagging and named entity recognition.

How do language models work in terms of predicting the next word in a sentence?

Language models estimate the probability of the next word based on the previous words by using data, often relying on sequences or n-grams to calculate the likelihood of the next word.

What is an n-gram, and how is it used in language models?

An n-gram is a sequence of 'n' words, and it is used to estimate the probability of a word occurring given the previous 'n-1' words. Common examples are bigrams (2 words), trigrams (3 words), and 4-grams.

What are the challenges when estimating the probability of long sequences in language models?

The challenge lies in the fact that long sequences may never occur entirely in the training data. This leads to complications in probability estimation, requiring techniques like breaking sequences into smaller pieces.

What is the chain rule of probability in the context of language models?

The chain rule of probability allows breaking down the probability of a sequence of words into conditional probabilities of each word given the preceding ones. This helps simplify the calculation of long sequences.

What does the Markov assumption state, and why is it important?

The Markov assumption states that the probability of the next word only depends on the previous 'M' words, rather than the entire history of the sequence. This simplification makes probability estimation more manageable.

What are some of the problems that arise when estimating probabilities in language models?

One problem is that the first word in a sequence may have an undefined probability, and another is that the model might not properly normalize probabilities across sequences of different lengths.

How can the issue of undefined probability for the first word in a sequence be addressed?

This can be fixed by adding a special token (like a 'start' token) that accounts for the beginning of a sequence, allowing for a defined probability for the first word.

What role does the fake token play in the probability normalization process?

The fake token helps ensure that the sum of probabilities over all possible sequences is equal to one. It provides a termination point for the sequence and prevents the model from overfitting to incomplete sequences.

How does introducing the fake token improve the model’s ability to handle sequences of different lengths?

By adding the fake token at the end, the model can generate a termination point for sequences. This allows the probability mass to be distributed correctly, ensuring that all possible sequences sum to one and maintaining proper normalization.