Contextualized embeddings, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer), have significantly improved natural language understanding tasks due to their ability to capture rich contextual information from the surrounding words. Here's how they achieve this:

  1. Capturing contextual information: Traditional word embeddings, like Word2Vec and GloVe, assign fixed vector representations to words, regardless of the context in which they appear. In contrast, contextualized embeddings consider the entire context of a word, generating different embeddings for the same word depending on its surrounding context. This helps capture the nuances and multiple meanings of a word within different sentences.

  2. Better word representations: Contextualized embeddings provide more meaningful word representations. By considering the context, they can capture morphological and syntactic variations, as well as capture the relationship between word senses and their overall meaning. This allows models to better differentiate between homonyms and polysemous words.

  3. Improved language understanding: Contextualized embeddings provide a better understanding of complex linguistic structures and relationships. The pre-training phase of models like BERT and GPT involves exposure to large amounts of text data, allowing the learning of diverse and intricate language patterns. This deep understanding allows the models to perform better on tasks such as parsing, word sense disambiguation, and sentence completion.

  4. Better sentence representations: In addition to word-level embeddings, contextualized embeddings also provide improved sentence representations. By considering the surrounding context of all words in a sentence, these embeddings can capture the syntax, semantics, and discourse-level information. This is particularly useful in tasks like sentiment analysis, text classification, and paraphrasing, where the overall meaning and structure of the sentence are essential.

  5. Transfer learning: Contextualized embeddings, with their ability to capture rich contextual information, can be used as pretrained models in transfer learning settings. These embeddings can be fine-tuned on smaller, domain-specific datasets to achieve excellent performance on a wide range of natural language understanding tasks without extensive task-specific training.

  Overall, contextualized embeddings have proven to be effective in improving natural language understanding tasks by capturing contextual information, providing better word and sentence representations, and enabling transfer learning paradigms.