Google Bert: What's Changing From Now On

[shaownhasan]

I must admit, given my background as a linguist, that Google Bert is perhaps the Google update that has most stimulated my studies on the subject. We can define it as a sort of compromise between the semantic web and the semantics of language , remembering and emphasizing that in reality the two things have little in common.

Like never before this time I had to study a lot because the topic is not at all simple, on the contrary it presents enormous difficulties of understanding. The main problem is that in most online sources you can only find how turn leads into sales with overseas chinese in worldwide data Google uses this technology, but not what this technology actually is . I tried to dig deeper, I hope that the effort will be appreciated by you.

What exactly does BERT mean?
BERT (Bidirectional Encoder Representations from Transformers) is basically a recently published paper by researchers at Google AI Language. It has created a stir in the Machine Learning community by presenting cutting-edge results on a wide range of NLP tasks, including Question Answering (SQuAD v1.1), Natural Language Inference (MNLI), and several other types.

BERT's main technical innovation is to apply Transformer bidirectional training, a fairly well-known model for understanding, to language modeling. This is in contrast to previous efforts that have looked at a left-to-right text sequence or a combined left-to-right and right-to-left alignment.

The results of the paper show that a bidirectionally set language model can have a deeper sense of context and language flow than single-direction language models.