How can you implement sequence-level beam search with NLTK utilities

Question

With the help of code snippets in Python programming language, can you tell me how I can implement sequence-level beam search with NLTK utilities?

Answer 1

To implement sequence-level beam search using NLTK utilities, you can use a custom beam search algorithm that evaluates and selects the top sequences based on scores (e.g., log-likelihood). Here is the code snippet you can refer to:

In the above code, we are using the following 

• Score Function: A custom scoring function (score_sequence) ranks sequences (for demonstration, it's based on the total length of words).
• Beam Search:
  • Start with an initial sequence.
  • At each step, generate new candidates (extend each sequence with new words).
  • Keep only the top beam_width sequences based on their scores.
• Final Output: The top beam_width sequences after the max length is reached.

The output of the above code would be:

Hence, this is a simplified version; in real applications, you would replace the score_sequence with a model's likelihood score and generate actual tokens (e.g., words).