direct quotation model
The model is that chatbot does not generate the conversation, but simply quotes a passage from a book or other source. 2018-09-30
I was trying to do with learning.
Changed the rule base to separate by brackets, curly braces, punctuation, etc.
After all, the most frequent bad pattern is that "broken sentences" are produced, so I just used punctuation marks to separate them.
If anything, it would make more sense for seq2seq to take this "one sentence" as input data and output it by shaving off the indicator words, etc. Leave it bare as the initial value.
Word-based input, output empty string when word to be deleted
I mean, would you prefer a binary classification of "bare or deleted"?
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Model of the quotation itself
Model of citation starting point
Model of Citation End Points
feature value
Contains key phrase 1/0
The word boundary, 1/0
It is of moderate length. What is the definition of moderate?
Punctuation immediately preceding 1/0
The model for the start and end points should be something that can be computed using only local features.
Context of surrounding few words
The appropriate length of the quotation can be made as a model of the quotation itself
The model of the goodness of the quotation itself is made by LSTM.
We can use both as probability models and then multiply them together.
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