DHAP

Source code of SIGIR2021 Long Paper:

One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles .

Preinstallation

First, install the python packages in your Python3 environment:

  git clone https://github.com/zhengyima/DHAP.git DHAP
  cd DHAP
  pip install -r requirements.txt

Then, you should download the pre-trained word embeddings to initialize the model training. We provide two word embeddings in the Google Drive:

• sgns.weibo.bigram-char, folloing Li et al., Chinese word embeddings pre-trained on Weibo. Google Drive
• Fasttext embeddings, English word embedding pre-trained on Reddit set. Google Drive

You can pre-train your own embeddings(with the same format, i.e., the standard txt format), and use it in the model.

After downloading, you should put the embedding file to the path EMB_FILE.

Data

You should provide the dialogue history of users for training the model. For convenience, we provide a very small subset of PChatbot in the data/ as the demo data. In the direcotry, each user's dialogue history is saved in one text file. Each line in the file should contain post text, user id of post, post timestamp, response text, user id of response, response timestamp, _, _ , with tab as the seperator.

You can refer to seq2seq/dataset/perdialogDatasets.py for more details about the data processing.

If you are interested in the dataset PChatbot, please go to its official repository for more details.

Model Training

We provide a shell script scripts/train_chat.sh to start model pre-training. You should modify the DATA_DIR and EMB_FILE to your own paths. Then, you can start training by the following command:

bash scripts/train_chat.sh

The hyper-parameters are defined and set in the configParser.py.

After training, the trained checkpoints are saved in outputs. The inferenced result is saved in RESULT_FILE, which you define in bash scripts/train_chat.sh

Evaluating

For calculating varities of evaluation metrics(e.g. BLEU, P-Cover...), we provide a shell script scripts/eval.sh. You should modify the EMB_FILE to your own path, then evaluate the results by the following command:

bash scripts/eval.sh

Citations

If our code helps you, please cite our work by:

@inproceedings{DBLP:conf/sigir/madousigir21,
     author = {Zhengyi Ma and Zhicheng Dou and Yutao Zhu Hanxun Zhong and Ji-Rong Wen}, 
     title = {One Chatbot Per Person: Creating Personalized Chatbots based onImplicit User Profiles}, 
     booktitle = {Proceedings of the {SIGIR} 2021}, 
     publisher = {{ACM}}, 
     year = {2021}, 
     url = {https://doi.org/10.1145/3404835.3462828}, 
     doi = {10.1145/3404835.3462828}}

Links

• Pytorch
• PChatbot Dataset