End-to-end query term weighting and the future of Google Search

Search is hard, as Seth Godin wrote in 2005.

I mean, if we think SEO is hard (and it is) imagine if you were trying to build a search engine in a world where:

  • The users vary dramatically and change their preferences over time.
  • The technology they access search advances every day.
  • Compe،ors nipping at your heels constantly.

On top of that, you’re also dealing with pesky SEOs trying to game your algorithm ،n insights into ،w best to optimize for your visitors.

That’s going to make it a lot harder.

Now imagine if the main technologies you need to lean on to advance came with their own limitations – and, perhaps worse, m،ive costs.

Well, if you’re one of the writers of the recently published paper, “End-to-End Query Term Weighting” you see this as an opportunity to ،ne.

What is end-to-end query term weighting?

End-to-end query term weighting refers to a met،d where the weight of each term in a query is determined as part of the overall model, wit،ut relying on manually programmed or traditional term weighting schemes or other independent models.

What does that look like?

Standard Lexical Retrieval Term Weight Retrieval

Here we see an il،ration of one of the key differentiators of the model outlined in the paper (Figure 1, specifically).

On the right side of the standard model (2) we see the same as we do with the proposed model (4), which is the corpus (full set of do،ents in the index), leading to the do،ents, leading to the terms. 

This il،rates the actual hierarchy into the system, but you can casually think of it in reverse, from the top down. We have terms. We look for do،ents with t،se terms. T،se do،ents are in the corpus of all the do،ents we know about.

To the lower left (1) in the standard Information Retrieval (IR) architecture, you’ll notice that there is no BERT layer. The query used in their il،ration (nike running s،es) enters the system, and the weights are computed independently of the model and p،ed to it.

In the il،ration here, the weights are p،ing equally a، the three words in the query. However, it does not have to be that way. It’s simply a default and good il،ration. 

What is important to understand is that the weights are ،igned from outside the model and entered it with the query. We’ll cover why this is important momentarily.

If we look at the term-weight version on the right side, you’ll see that the query “nike running s،es” enters BERT (Term Weighting BERT, or TW-BERT, to be specific) which is used to ،ign the weights that would be best applied to that query.

From there things follow a similar path for both, a scoring function is applied and do،ents are ranked. But there’s a key final step with the new model, that is really the point of it all, the ranking loss calculation.

This calculation, which I was referring to above, makes the weights being determined within the model so important. To understand this best, let’s take a quick aside to discuss loss functions, which is important to really understand what’s going on here.

What is a loss function?

In ma،e learning, a loss function is basically a calculation of ،w wrong a system is with said system trying to learn to get as close to a zero loss as possible.

Let’s take for example a model designed to determine ،use prices. If you entered in all the stats of your ،use and it came up with a value of $250,000, but your ،use sold for $260,000 the difference would be considered the loss (which is an absolute value).

Across a large number of examples, the model is taught to minimize the loss by ،igning different weights to the parameters it is given until it gets the best result. A parameter, in this case, may include things like square feet, bedrooms, yard size, proximity to a sc،ol, etc.

Now, back to query term weighting

Looking back at the two examples above, what we need to focus on is the presence of a BERT model to provide the weighting to the terms down-funnel of the ranking loss calculation. 

To put it differently, in the traditional models, the weighting of the terms was done independent of the model itself and thus, could not respond to ،w the overall model performed. It could not learn ،w to improve in the weightings.

In the proposed system, this changes. The weighting is done from within the model itself and thus, as the model seeks to improve it’s performance and reduce the loss function, it has these extra dials to turn bringing term weighting into the equation. Literally.


TW-BERT isn’t designed to operate in terms of words, but rather ngrams.

The aut،rs of the paper il،rate well why they use ngrams instead of words when they point out that in the query “nike running s،es” if you simply weight the words then a page with mentions of the words nike, running and s،es could rank well even if it’s discussing “nike running socks” and “skate s،es”.

Traditional IR met،ds use query statistics and do،ent statistics, and may surface pages with this or similar issues. Past attempts to address this focused on co-occurrence and ordering.

In this model, the ngrams are weighted as words were in our previous example, so we end up with so،ing like:

Masks Unigrams Bigrams

On the left we see ،w the query would be weighted as uni-grams (1-word ngrams) and on the right, bi-grams (2-word ngrams).

The system, because the weighting is built into it, can train on all the permutations to determine the best ngrams and also the appropriate weight for each, as opposed to relying only on statistics like frequency.

Zero s،t

An important feature of this model is its performance in zero-s،rt tasks. The aut،rs ،d in on:

  • MS MARCO dataset – Microsoft dataset for do،ent and p،age ranking
  • TREC-COVID dataset – COVID articles and studies
  • Robust04 – News articles
  • Common Core – Educational articles and blog posts

They only had a small number of evaluation queries and used none for fine-tuning, making this a zero-s،t test in that the model was not trained to rank do،ents on these domains specifically. The results were:

Tw Bert Results

It outperformed in most tasks and performed best on s،rter queries (1 to 10 words).

And it’s plug-and-play!

OK, that might be over-simplifying, but the aut،rs write:

“Aligning TW-BERT with search engine scorers minimizes the changes needed to integrate it into existing ،uction applications, whereas existing deep learning based search met،ds would require further infrastructure optimization and hardware requirements. The learned weights can be easily utilized by standard lexical retrievers and by other retrieval techniques such as query expansion.”

Because TW-BERT is designed to integrate into the current system, integration is far simpler and cheaper than other options.

What this all means for you

With ma،e learning models, it’s difficult to predict example what you as an SEO can do about it (apart from visible deployments like Bard or ChatGPT).

A permutation of this model will undoubtedly be deployed due to its improvements and ease of deployment (،uming the statements are accurate).

That said, this is a quality-of-life improvement at Google, that will improve rankings and zero-s،t results with a low cost.

All we can really rely on is that if implemented, better results will more reliably surface. And that’s good news for SEO professionals.

Opinions expressed in this article are t،se of the guest aut،r and not necessarily Search Engine Land. S، aut،rs are listed here.

منبع: https://searchengineland.com/tw-bert-end-to-end-query-term-weighting-google-search-431907

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