Constrained dialogue generation

This codebase contains the code for constrained dialogue generation. We include files to run the approach as well as the public datasets we run experiments on.

Pre-process data

• ABCD
  • Download and extract the dataset https://github.com/asappresearch/abcd/blob/master/data/abcd_v1.1.json.gz into data/ABCD
• MultiWoz
  • Download and extract the dataset https://github.com/lexmen318/MultiWOZ-coref/blob/main/MultiWOZ2_3.zip into data/MultiWoz
  • Run the preprocessing script: cd data/MultiWoz and python preprocess_multiwoz.py --output-file multiwoz_processed.json
• TaskMaster-3
  • Download and extract the dataset svn checkout https://github.com/google-research-datasets/Taskmaster/trunk/TM-3-2020/data into data/TaskMaster
  • Run the preprocessing script: cd data/TaskMaster and python preprocess_taskmaster.py --output-file taskmaster_processed.json

Requirements

• Python 3.7.6 (this version is verified to run the code)
• pip install -r requirements.txt

Fine-tune models

• Example for ABCD
  • Train the customer model.

    • cd finetune
      python main.py --do-train --local-rank -1 --config-file configs/abcd_customer.ini
      

  • Evaluate the customer model.

    • python main.py --nodo-train --do-eval --local-rank -1 --config-file configs/abcd_customer.ini
      

  • Similarly, train and evaluate an agent model using these commands:

    • python main.py --do-train --local-rank -1 --config-file configs/abcd_agent.ini
      python main.py --nodo-train --do-eval --local-rank -1 --config-file configs/abcd_agent.ini
      

Build datastores

• Example for ABCD
  • Create the train datastore.

    • cd datastore
      model_path="Enter path to the customer model here"
      python knn_datastore.py \
             --build-datastore \
             --model_path "${model_path}" \
             --data-path ../data/ABCD/abcd_v1.1.json \
             --output-dir ../data/ABCD/DATASTORE \
             --split train \
             --finetuned \
             --fp16
      

  • Create the test datastore.

    • model_path="Enter path to the customer model here"
      python knn_datastore.py \
             --build-datastore \
             --model_path "${model_path}" \
             --data-path ../data/ABCD/abcd_v1.1.json \
             --output-dir ../data/ABCD/DATASTORE \
             --split test \
             --finetuned \
             --fp16
      

Run approaches

• Example for ABCD

  •   cd approaches
      model_path="Enter path to the customer model here"
      agent_model_path="Enter path to the agent model here"
      bash run_individual.sh \
           --run-approaches=wfirst,finetuned,prompt,dbs,cgmh,retrieve,windowfop \
           --MODEL-TYPE=finetuned \
           --MODEL-PATH="${model_path}" \
           --AGENT-MODEL-PATH="${agent_model_path}" \
           --config-file=../finetune/configs/abcd.ini \
           --data-dir=../data/ABCD/ \
           --save-dir=abcd_results
    

• Get results table

  • Run the jupyter notebook in approaches/get-latex-results-table.ipynb with the appropriate result directories.

• Plot graphs for simulated conversations

  • python plot.py --save_dir <results directory> --eval_type simulated

** You can follow a similar set of steps for the other datasets with the corresponding config files. **