Jun 22, 2022
In General Discussion
True contextual understanding comes from being able to Banner Design see all the words in a sentence at once and understanding how all the words impact the context of the other words in the sentence as well. The part of speech a particular word belongs to can literally change as the sentence develops. For example, although it is unlikely to be a query, if we take a spoken phrase that might well appear in natural conversation (albeit rarely): "I like how you like that he likes it." as Banner Design the sentence develops the part of speech to which the word "like" relates as the context builds around each mention of the word changes so that the word "like", although textually either the same word, is contextually dependent different parts of speech its place in the phrase or phrase. Old natural language training models were trained unidirectionally. The meaning of the word in a pop-up moved from left to right or right to left with a given number of words around the target word (the context Banner Design of the word or "this is society"). This meant that words not yet seen in context cannot be considered in a sentence and could actually change the meaning of other words in natural language. One-way moving popups therefore have the potential to miss some important changing contexts. For example, in the sentence: "Dawn, how are you?" The word “are” can be the target word and the Banner Design left context of “are” is “Dawn, how”. The correct context of the word is "you". BERT is able to look at both sides of a target word and the entire sentence simultaneously the way humans look at the whole context of a sentence rather than just looking at part of it. The entire sentence, left and right of a target word can be viewed simultaneously in context. Transformers / Transformer Architecture Most natural language Banner Design understanding tasks rely on probability predictions. What is the probability that this sentence relates to the following sentence, or what is the probability that this word is part of this sentence? The architecture of BERT and masked language modeling prediction systems are in part designed to identify ambiguous words that change the meaning of phrases and sentences and to identify the correct one. Learnings are increasingly being taken over by BERT systems. The Transformer uses fixation on Banner Design words in the context of all other words in phrases or sentences without which the phrase might be ambiguous. This fixed attention comes from an article titled 'Attention is all you need' (Vaswani et al, 2017), published a year earlier than the BERT research paper, with the Transformer application following integrated into BERT search. Essentially, BERT is able to look at all of the context within text cohesion by focusing attention on a given word in a sentence while also identifying all of the context of other words relative to the word. This is achieved simultaneously using transformers combined with two-way pre-training. This helps with a number of longstanding linguistic challenges for natural language understanding, including coreference resolution. Indeed, entities can be concentrated in Banner Design a sentence as the target word and their pronouns or the noun phrases that reference them are resolved to the entity or entities of the sentence or phrase. That way, the concepts and context of who, or what, a particular phrase specifically relates to, aren't lost along the way. In addition, focused attention also Banner Design helps disambiguate polysemous words and homonyms using probability prediction/weighting based on the entire context of the word in context with all other words in the sentence. The other words are given a weighted attention score to indicate how much each adds to the context of the target word as a representation of "meaning". Words in a sentence about "bank" that add clear and plain context such as "deposit" would carry more weight in a sentence about "bank" (financial institute) to resolve the context of representation to that of an institute financial.