Contextualized embeddings, such as those produced by models like BERT and GPT, capture syntactic information by leveraging the surrounding context of a word in a sentence. These embeddings are able to assign different representations to the same word depending on its syntactic role in a given context.

  1. Bidirectional Attention: Contextualized embeddings take into account both the left and right context of each word. For example, in the sentence "I saw a dog and a cat," the word "saw" would have different contextualized embeddings depending on whether it is followed by "a" or "and". This bidirectional attention allows the model to capture the syntactic relationship between words.

  2. Transformer Architecture: Contextualized embeddings are typically generated using transformer-based models. Transformers consist of self-attention mechanisms that assign different weights to different words in a sentence based on their importance for encoding syntactic information. By attending to different parts of the sentence, transformers can effectively capture the syntactic structure.

  3. Contextualized Training: Contextualized embeddings are trained in a way that considers the entire sentence or paragraph, rather than just individual words. This allows the model to learn the dependencies and relationships between words and their syntactic roles. During training, the model predicts the next word given the previous words, which encourages the model to capture the syntactic and semantic relationships in the context.

  4. Fine-tuning: Contextualized embeddings are usually fine-tuned on downstream tasks such as text classification or named entity recognition, where syntactic information is crucial for accurate predictions. Fine-tuning further enhances the model's ability to capture and utilize syntactic information in a task-specific manner.

  By considering both the local and global context of a word, contextualized embeddings can effectively capture syntactic information. These embeddings have been shown to improve the performance of various natural language processing tasks that rely on understanding sentence structures and relationships between words.