BERT stands for Bidirectional Encoder Representations Transformers.
It is an open-source program that any Python lover can train his question-answer system.
BERT is bidirectional, which makes it unique to other algorithms that are usually unidirectional.
But, Alim, please explain with an example of a unidirectional and bidirectional approach.
In the unidirectional approach, Google only evaluates a sentence’s previous context, not the following context.
The word “Bank” has different meanings: the river bank and the money bank.
When we say, “I accessed the bank account.” In the unidirectional approach, Google judges the word BANK based on the “I accessed” only.
In the bidirectional approach, Google also looks at the “account” to understand its relevance.
Still didn’t get it?
Let’s take another example.
Sentence A: The man went to the store.
Sentence B: He bought a gallon of milk.
In this example, BERT knows “the man bought a gallon of milk.” from the previous and the following sentence.
Sentence A: the man went to the store.
Sentence B: penguins are flightless.
In this example, Google knows the second sentence is not linked with the first sentence; that is why Google has labeled it as “It is not the next sentence.”
According to Google research, BERT is better than human beings in understanding the content.
Human being has an accuracy of 91.221, but BERT achieves 93.160!
What Do I Believe?
BERT is aimed at content. You should write grammatically correct English to make sense of the content.
The flow from one sentence to another is more essential than ever before to get a featured snippet and overall ranking of your article.
We should wait for a week until the update is fully rolled out.
Before that, I will not make a significant hypothesis about the update because I haven’t seen a substantial change in traffic on my client sites: one saw a significant improvement in traffic.
SOURCES TO LEARN MORE ABOUT BERT: