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One 'easy' way would be to have some sort of conversational memory, where you track what the user has said already. I don't know how complex your patterns are, but if you could recognise names and track references, you could try and build up a mental model of the user's relationships with other people, and perhaps refer to that in your bots responses. The ...


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Simply put, the attention mechanism is loosely inspired on well, attention. Consider we are attempting machine translation on the following sentence: "The dog is a Labrador." If you were to ask someone to pick out the key words of the sentence, i.e. which ones encode the most meaning, they would likely say "dog" and "Labrador." Articles like "the" and "a" ...


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In short, the Jacobian matrix is a generalization of the gradient for vector-valued functions. Recall that the gradient is a vector of partial derivatives of a multi-variable function. So, consider a multi-variable function of the form $f: \mathcal{X}_1 \times \mathcal{X}_2 \times \dots \times \mathcal{X}_N \rightarrow \mathcal{Y}$. The output of this ...


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Did you mean: How do you use a pre-trained BERT model in a feature-based setting to get pre-trained word contextual embeddings? Here is the BERT paper. I highly recommend you read it. Firstly, by sentences, we mean a sequence of word embedding representations of the words (or tokens) in the sentence. Word embeddings are the vectors that you mentioned,...


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