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Conchylicultor / DeepQA

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot
Apache License 2.0
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Diversify output by generating N-best candidates #34

Open eschnou opened 7 years ago

eschnou commented 7 years ago

The current implementation always output the most probable answer, which can lead to boring, repetitive, safe answers such as "i don't know". Solving this requires to generate not a single answer but a whole list of the N-best candidates. We can then randomly pick one answer, or use different loss functions to choose the best candidate. For example implementing the strategies described here.

Generating the N-best output in seq2seq requires to perform a beam search on the decoder output. It seems it has already been implemented in this gist and extensively discussed on this tensorflow issue.

Conchylicultor commented 7 years ago

An other way to get diverse answer I had thought of was to sample the answer from the softmax outputs as done in https://github.com/karpathy/char-rnn . In theory, it should be simpler to implement as soon this issue will be solved https://github.com/tensorflow/tensorflow/issues/5391.

eschnou commented 7 years ago

@Conchylicultor I don't think this will work in our case. By sampling the output soft-max you only sample the last output, so you will get a list of candidates for the last word only. The way I understand seq2seq is that the output sequence is coming from the sequence of decoders, not just the final decoder output. The case you refer too is not a sequence decoder but just a RNN in which they are only interested by the final output , which is the prediction of one character. Then softmax sampling makes sense. Makes sense ?

Conchylicultor commented 7 years ago

With the loop_function parameter, we would sample dynamically the next word at every time step. See this previous answer for more details https://github.com/Conchylicultor/DeepQA/issues/17#issuecomment-258221849. On the seq2seq model used here, the sequence decoder is just a RNN which take the encoder output as state input so it's really similar to what karpathy did.

Edit: the loop_function would need to look something like that https://github.com/Conchylicultor/MusicGenerator/blob/master/deepmusic/modules/loopprocessing.py#L58

eschnou commented 7 years ago

Ha ok, I was still focusing on generating n-best candidate solutions. With your approach, you effectively introduce variation but still only generate one output. Already a good start indeed to avoid repetition, but unfortunately not sufficient to generate n-best list and apply a different scoring functions.

Conchylicultor commented 7 years ago

Yes, indeed, this softmax sampling won't be enough to generate the N-best list. That would require to use beam search as you proposed.