https://github.com/asappresearch/constrained-dialogue-generation
https://github.com/asappresearch/constrained-dialogue-generation
Last synced: about 19 hours ago
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- Host: GitHub
- URL: https://github.com/asappresearch/constrained-dialogue-generation
- Owner: asappresearch
- Created: 2022-05-03T16:35:56.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-05-04T02:52:58.000Z (almost 3 years ago)
- Last Synced: 2025-05-01T12:48:03.982Z (about 20 hours ago)
- Language: Python
- Size: 60.5 KB
- Stars: 6
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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 --eval_type simulated`
### ** You can follow a similar set of steps for the other datasets with the corresponding config files. **