简单的理解:在nlp里面的一个sequence,1)首先,每个word通过W_v变为新的表达,即value; 2)每个word的value 都可以用全局信息(所有word的value)的组合(ex:线性组合)来得到新的对应于这个word的表达。那么这个word就认为是query,所有word是key;线性组合的权重就是attention;3)为了计算query和key之间的相关性;我们可以...
根据(8)注意力的权重分数(attention scores)取决于编码RNN状态 和解码RNN状态 。则获取attention scores 需要计算这个网络 次,其中编码序列长度 ,解码序列长度 。 可以考虑把 和 先投影到同一个空间,再定义个这个空间内的测度(如点乘)来计算attention scores[2]。除了用“Dot-Product Attention”,也可以用其他测度,...
To compute such impor- tance scores, the attention mechanism summarizes the source side information in the encoder RNN hidden states (i.e., ht), and then builds a context vector for a target side word up- on a subsequence representation of the source sentence, since ht actually summarizes ...
Using word attention scores, we can find out how relevant a single word is to a particular skill. As a result of these attention scores, we obtain more relevant translations for each skill. We then use these translations to bridge the lexical gap and improve expert retrieval results. ...
That is, the dot product of the query vector with the key vector of the respective word we're scoring. Multi-head Attention In the previous description the attention scores are focused on the whole sentence at a time, this would produce the same results even if two sentences contain the ...
The mode of word report (partial vs. full) was manipulated between subjects. No difference was found in either mode between the gap detection scores in the expected and the unexpected words. Neither of those scores differed from the score for trials in which both words were unexpected. The ...
a target side word. To compute such importance scores, the attention mechanism summarizes the source side information in the encoder RNN hidden states (i.e., ht), and then builds a context vector for a target side word upon a subsequence representation of the source sentence, since ht ...
When interpreting the results, therefore, the coefficients should be read as showing the effect of a one standard deviation (SD) variation in the corresponding variable in terms of SD units. The transformed scores were stationary using the AD Fuller test (see SI Section 3 for details). Both ...
The attention scores are then used as weights for a weighted average of all words’ representations which is fed into a fully-connected network to generate a new representation for “bank”, reflecting that the sentence is talking about a river bank.[4] In the transformer block, "Add & ...
According to the relevant data concerning ESG in the Bloomberg database, it is divided into the comprehensive score ESG and sub-scores ESG_E, ESG_S and ESG_G. The comprehensive score of ESG is calculated from 3 sub-scores, 21 sub-items and 122 specific indicators according to the ...