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 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. This the code for 'Build a Chatbot' on Youtube

Source: http://googleresearch.blogspot.ca/2015/11/computer-respond-to-this-email.html

1. Install Torch.

2. 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

3. Download the Cornell Movie-Dialogs Corpus and extract all the files into data/cornell_movie_dialogs.

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. Use the --dataset NUMBER option to control the size of the dataset. Training on the full dataset takes about 5h for a single epoch. The model will be saved to data/model.t7 after each epoch if it has improved (error decreased).

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.

To load the model and have a conversation:

th -i eval.lua --cuda # Skip --cuda if you didn't train with it
# ...
th> say "Hello."

Credit for the vast majority of code here goes to Marc-André Cournoyer. I've merely created a wrapper around all of the important functions to get people started.