What are context-dependent embeddings?

What are context-dependent embeddings?

• A. Dynamic vector representations of words that change based on surrounding context, modern models follow this
• B. Static vector representations that do not change with context
• C. A method only used for image data
• D. A technique for compressing models

Answer: (A)

Context-dependent embeddings are dynamic vector representations of words that vary with surrounding context. Unlike static embeddings, where each word has a single fixed vector, contextualized embeddings are computed for each occurrence by incorporating the other words in the sentence (and sometimes the broader text) through mechanisms like self-attention in transformers. This means the same word can have different representations in different sentences, which helps resolve nuances and multiple meanings of a word, such as “bank” in “river bank” versus “financial bank.” Modern models like BERT or GPT produce these contextual embeddings, reflecting how language usage changes with context. The other options don’t fit because one describes static embeddings that ignore context, another wrongly limits the concept to image data, and the last describes model compression, which is unrelated to how word representations are formed.

Context-dependent embeddings are dynamic vector representations of words that vary with surrounding context. Unlike static embeddings, where each word has a single fixed vector, contextualized embeddings are computed for each occurrence by incorporating the other words in the sentence (and sometimes the broader text) through mechanisms like self-attention in transformers. This means the same word can have different representations in different sentences, which helps resolve nuances and multiple meanings of a word, such as “bank” in “river bank” versus “financial bank.” Modern models like BERT or GPT produce these contextual embeddings, reflecting how language usage changes with context. The other options don’t fit because one describes static embeddings that ignore context, another wrongly limits the concept to image data, and the last describes model compression, which is unrelated to how word representations are formed.