This is an attempt at implementing Sequence to Sequence Learning with Neural Networks (seq2seq) and reproducing the results in A Neural Conversational Model (aka the Google chatbot).
The Google chatbot paper became famous after cleverly answering a few philosophical questions, such as:
Human: What is the purpose of living?
Machine: To live forever.
The model is based on two LSTM layers. One for encoding the input sentence into a "thought vector", and another for decoding that vector into a response. This model is called Sequence-to-sequence or seq2seq.
Source: http://googleresearch.blogspot.ca/2015/11/computer-respond-to-this-email.html
In this experiment, we train the seq2seq model with movie dialogs from the Cornell Movie-Dialogs Corpus. The lines are shortened to the first sentence.
Here's a sample conversation after training for 20 epoch with 50000 examples, using the following command:
th train.lua --cuda --dataset 50000 --hiddenSize 1000(Took 3 days to train on my GeForce GTX 780M.)
For OpenCL, use --opencl instead of --cuda. To train on CPU, don't provide any of those two.
WARNING: I can no longer reproduce those results. The cause seems to be a change in one of dependencies. But I'm currently working on a new implementation based on harvardnlp/seq2seq-attn.
me: Hello?
bot: Hi.me: How are you?
bot: I'm fine.me: What's your name?
bot: It's hard to describe.me: How so?
bot: I'm not sure.me: What color is the sky?
bot: It's blue.me: What is your job?
bot: It's not that i'm a fucking werewolf!me: What is the purpose of life?
bot: A gift.me: Are you intelligent?
bot: Yes, well...me: Are you a machine?
bot: That's a lie.me: Are you human?
bot: No, i'm not.me: What are you?
bot: I'm not sure.me: Do you plan on taking over the world?
bot: No, i don't.
Phew! That was close. Good thing I didn't train it on the full dataset. Please experiment responsibly.
(Disclaimer: nonsensical responses have been removed.)
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Install the following additional Lua libs:
luarocks install nn luarocks install rnn luarocks install penlight
To train with CUDA install the latest CUDA drivers, toolkit and run:
luarocks install cutorch luarocks install cunn
To train with opencl install the lastest Opencl torch lib:
luarocks install cltorch luarocks install clnn
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Download the Cornell Movie-Dialogs Corpus and extract all the files into data/cornell_movie_dialogs.
th train.lua [-h / options]The model will be saved to data/model.t7 after each epoch if it has improved (error decreased).
--opencluse opencl for computation (requires torch-cl)--cudause cuda for computation--gpu [index]use the nth GPU for computation (eg. on a 2015 MacBook--gpu 0results in the Intel GPU being used while--gpu 1uses the far more powerful AMD GPU)-- dataset [size]control the size of the dataset--maxEpoch [amount]specify the number of epochs to run
To load the model and have a conversation:
th eval.luaMIT License
Copyright (c) 2016 Marc-Andre Cournoyer
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