Neural Conversational Model in Torch

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.

How it works

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.

Sample conversation

Here's a sample conversation after training for 10 epoch with only 5000 examples, using the following command:

th train.lua --cuda --dataset 5000 --hiddenSize 1000

for opencl, you can use the following command:

th train.lua --opencl --dataset 5000 --hiddenSize 1000

me: Hi
bot: Hey.

me: What's your name?
bot: Pris.

me: How old are you?
bot: Thirty five eleven.

me: What's 2 + 2?
bot: Nothing.

me: That's funny.
bot: No.

me: Where are you from?
bot: Helsinki, there!

me: That's a nice place.
bot: Yes.

me: How long have you been living in Helsinki?
bot: Thirty years.

me: Talk to you later.
bot: What?

me: I'm leaving.
bot: Leaving what?

me: Leaving this conversation.
bot: Yes.

(Disclaimer: nonsensical responses have been removed.)

The results are fun but far less impressive than in the paper. This is probably because of the extremely small dataset and small network I used.

Sadly, my graphic card doesn't have enough memory to train larger networks.

If you manage to run it on a larger network do let me know!

Installing

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

Training

th train.lua [-h / options]

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).

Testing

still have problem for cltorch

To load the model and have a conversation:

th -i eval.lua
# ...
th> say "Hello."

License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

rsarxiv / neuralconvo