Google announced a remarkable ranking framework called Term Weighting BERT (TW-BERT) that improves search results and is easy to deploy in existing ranking systems.
Although Google has not confirmed that it is using TW-BERT, this new framework is a breakthrough that improves ranking processes across the board, including in query expansion. It’s also easy to deploy, which in my opinion, makes it likelier to be in use.
TW-BERT has many co-authors, among them is Marc Najork, a Distinguished Research Scientist at Google DeepMind and a former Senior Director of Research Engineering at Google Research.
He has co-authored many research papers on topics related to ranking processes, and many other fields.
Among the papers Marc Najork is listed as a co-author:
- On Optimizing Top-K Metrics for Neural Ranking Models – 2022
- Dynamic Language Models for Continuously Evolving Content – 2021
- Rethinking Search: Making Domain Experts out of Dilettantes – 2021
- Feature Transformation for Neural Ranking Models – – 2020
- Learning-to-Rank with BERT in TF-Ranking – 2020
- Semantic Text Matching for Long-Form Documents – 2019
- TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank – 2018
- The LambdaLoss Framework for Ranking Metric Optimization – 2018
- Learning to Rank with Selection Bias in Personal Search – 2016
What is TW-BERT?
TW-BERT is a ranking framework that assigns scores (called weights) to words within a search query in order to more accurately determine what documents are relevant for that search query.
TW-BERT is also useful in Query Expansion.
Query Expansion is a process that restates a search query or adds more words to it (like adding the word “recipe” to the query “chicken soup”) to better match the search query to documents.
Adding scores to the query helps it better determine what the query is about.
TW-BERT Bridges Two Information Retrieval Paradigms
The research paper discusses two different methods of search. One that is statistics based and the other being deep learning models.
There follows a discussion about the benefits and the shortcomings of these different methods and suggest that TW-BERT is a way to bridge the two approaches without any of the shortcomings.
Is Google Using TW-BERT In their Ranking Algorithm?
As mentioned earlier, deploying TW-BERT is relatively easy.
In my opinion, it’s reasonable to assume that the ease of deployment increases the odds that this framework could be added to Google’s algorithm.
That means Google could add TW-BERT into the ranking part of the algorithm without having to do a full scale core algorithm update.
Aside from ease of deployment, another quality to look for in guessing whether an algorithm could be in use is how successful the algorithm is in improving the current state of the art.
There are many research papers that only have limited success or no improvement. Those algorithms are interesting but it’s reasonable to assume that they won’t make it into Google’s algorithm.
The ones that are of interest are those that are very successful and that’s the case with TW-BERT.
TW-BERT is very successful. They said that it’s easy to drop it into an existing ranking algorithm and that it performs as well as “dense neural rankers”
Original Source: searchenginejournal.com
