Contextualized embeddings are a type of word representation that takes into account the context in which a word appears in a sentence. Unlike traditional word embeddings, such as word2vec or GloVe, which assign a fixed embedding to each word, contextualized embeddings provide a unique representation for each occurrence of a word based on its surrounding words.

  To handle word order in a sentence, contextualized embeddings rely on models that are designed to capture the contextual meaning of words. One popular model for generating contextualized embeddings is the Transformer-based architecture, which includes models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer).

  These models use a self-attention mechanism to capture dependencies between words in a sentence. By attending to different parts of the sentence, the model can understand and represent the relationships between words and their respective positions. This allows the contextualized embeddings to capture not only the individual meaning of each word but also the influence of its surrounding words.

  During the training process, these models are exposed to a large amount of text data and learn to predict the next word in a sentence based on the preceding words. This task forces the models to understand the syntax and semantics of the text, including the word order. As a result, the contextualized embeddings are rich in information about the context and word order.

  When generating contextualized embeddings for a specific sentence, the model takes into account all the words in the sentence, scanning them in a sequential manner and updating the representations of each word. This process allows the model to encode the information about word order and context into the embeddings.

  In summary, contextualized embeddings handle word order in a sentence by using models that capture the relationships between words and their positions. Through the use of self-attention mechanisms and training on large amounts of text data, these models can generate contextualized embeddings that reflect the influence of word order on the meaning of words in a sentence.