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I have developed a chatbot, which is basically a seq2seq LSTM network. Which can generate text based on input text. But the problem I am having right now is it is not topic aware.
As an example :

Input : Wanna go to the stadium ? Topic: Football

Answer: No , I don't like football.

What I am planning to do , is to tokenize each possible topic

As an example {1: "Football",2: "Food" ..........}

But don't know how to embed this tokenized topic in training .

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1 Answer 1

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Try this approach (not embedded but filtering your model) with Topic modeling, that could be used to filter the results of your model by topic:

  1. Separate each doc in several sentences
  2. Obtain the best topics ( occur > 3, chars > 3 ) from sentences if you do not have an idea of topics to declare (LatentDirichlet Allocation)
  3. Discard some topics out of interest

Finally assign a dominant topic to every sentence and filter results by topic.

I have found a reference from steps 1-3: https://medium.com/@actsusanli/when-topic-modeling-is-part-of-the-text-pre-processing-294b58d35514

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