This is based on Facebook's paper Deal or No Deal? End-to-End Learning for Negotiation Dialogues. I have implemented the basic process for training and interactive chat using the Tensorflow contrib.seq2seq Decoder framework.
<< Still WIP - Currently not working >>
- pretraining on predicting words
- pretraining including input context vector to each word
- predict outputs using attention layer over complete conversation
- create loss combining word loss and output reward loss
- rl training on combined loss
- encode opponent words, generate your own
- negotiation chatbot
The following are examples of how to use the applications. Get full help with
--help option on any of the programs.
The original dataset was provided by Facebook github End-to-End Negotiator. To transform input data into tensorflow Example format, an example:
python process_input.py --input=data/train.txt --model_dir=data
To pretrain the model on predicting words only:
python negotiator.py \
--pretrain \
--train_records=data/train.txt.tfrecords \
--vocab_file=data/train.txt.vocab \
--output_vocab=data/train.txt.outputs \
--embedding_dimension=100 \
--num_oov_vocab_buckets=10 \
--model_dir=data/model \
--learning_rate=0.01 \
--batch_size=128 \
--train_steps=1000 \
To train the model on goals:
python negotiator.py \
--train_goals \
--train_records=data/train.txt.tfrecords \
--vocab_file=data/train.txt.vocab \
--output_vocab=data/train.txt.outputs \
--model_dir=data/model \
--learning_rate=0.01 \
--batch_size=128 \
--train_steps=1000
To chat:
python negotiator.py \
--chat \
--vocab_file=data/train.txt.vocab \
--output_vocab=data/train.txt.outputs \
--model_dir=data/model