Google introduces Term Weight-Bert(TW-BERT), a new ranking framework that uses deep learning to improve the relevance of search results. TW-BERT is a hybrid approach that combines the strengths of traditional lexical retrievers with the power of deep learning models.
Traditional lexical retrievers are efficient at finding documents that contain the keywords in a search query. However, they do not consider the context of the query, which can lead to inaccurate results. Deep learning models, on the other hand, can understand the context of a query and assign weights to the relevant terms. However, they can be computationally expensive and difficult to train.
TW-BERT addresses these challenges by using a hybrid approach. It first uses a lexical retriever to find a set of candidate documents that contain the keywords in the search query. Then, it uses a deep learning model to assign weights to the terms in the query and to re-rank the candidate documents. This approach results in more accurate search results than either traditional lexical retrievers or deep learning models alone.
In addition to improving the relevance of search results, TW-BERT also makes it easier for users to find the information they are looking for. By understanding the context of a query, TW-BERT can suggest related queries and phrases that users may not have thought of. This can help users to refine their search and to find more relevant results.
TW-BERT is still under development, but it has the potential to revolutionize the way we search for information. It is easy to implement and is compatible with existing search systems. As a result, it is likely to be adopted by major search engines in the near future.