Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models

TL;DR

Neural machine translation systems can be improved by incorporating character-level models and word piece models, resulting in better translation accuracy.

Transcript

Okay. Hi, everyone. Let's get started again. Okay, so first of all let me just say a bit about Assignment 5. So Assignment 5 is coming out today. Um, it's a brand new assignment, so you guys are the guinea pigs for that. Um, and so what it's going to be, it essentially builds on Assignment 4. Um, so it's okay if you didn't do perfectly on Assignmen... Read More

Key Insights

• 🎰 Byte Pair Encoding (BPE) is an effective technique for expanding the vocabulary of a neural machine translation model.
• 🎚️ Hybrid models that combine word-level and character-level representations improve translation accuracy.
• ⌛ Character-level models can capture semantic similarity but require more training time and larger models.

Questions & Answers

Q: What is the purpose of Assignment 5 in the neural machine translation system?

Assignment 5 builds upon Assignment 4 and adds convolutional neural networks and subword modeling to enhance the translation system.

Q: How does the hybrid model improve translation accuracy?

The hybrid model combines word-level and character-level models to handle rare and unseen words, resulting in more accurate translation with a smaller model.

Q: Why is the character-level model slower than the word-level model?

The character-level model involves longer sequences and more back propagation through time, leading to slower processing times.

Summary & Key Takeaways

• Neural machine translation systems can benefit from incorporating character-level models and word piece models.

• Byte Pair Encoding (BPE) is an effective method for representing pieces of words and expanding the vocabulary of a model.

• Hybrid models that combine word-level and character-level representations have shown improved translation performance.