Generating Informative Responses with Controlled Sentence Function

Introduction

Sentence function is a significant factor to achieve the purpose of the speaker. In this paper, we present a novel model to generate informative responses with controlled sentence function. Given a user post and a sentence function label, our model is to generate a response that is not only coherent with the specified function category, but also informative in content.

This project is a tensorflow implementation of our work.

Dependencies

• Python 2.7
• Numpy
• Tensorflow 1.3.0

Quick Start

• Dataset

  Our dataset contains single-turn post-response pairs with corresponding sentence function labels. The sentence function labels of responses have been automatically annotated by a self-attentive classifier.

  Please download the Chinese Dialogue Dataset with Sentence Function Labels to data directory.

• Train

  python main.py

• Test

  python main.py --is_train=False --inference_path='xxx' --inference_version='yyy'

  You can test the model using this command. You may set the directory of test set with inference_path and the checkpoint to be used with inference_version. The generation result will be output to the 'xxx.out' file.

Details

Training

You can change the model parameters using:

--symbols xxx				size of full vocabulary
--topic_symbols xxx			size of topic vocabulary
--full_kl_step xxx			parameter of kl annealing
--units xxx 				size of hidden units
--embed_units xxx			dimension of word embedding
--batch_size xxx 			batch size in training process
--per_checkpoint xxx 			steps to save and evaluate the model
--data_dir xxx				data directory
--train_dir xxx				training directory

Paper

Pei Ke, Jian Guan, Minlie Huang, Xiaoyan Zhu. Generating Informative Responses with Controlled Sentence Function.
ACL 2018, Melbourne, Australia.

Please kindly cite our paper if this paper and the code are helpful.

License

Apache License 2.0