airdialogue-essentials 0.1

airdialogue package

AirDialogue

AirDialogue is a benchmark dataset for goal-oriented dialogue generation research. This python library contains a collection of tookits that come with the dataset.

• AirDialogue paper
• AirDialogue dataset
• Reference implementation: AirDialogue Model

What's New

• Jul 13,2020: Fixed a bug in BLEU evaluation. The current version gives higher BLEU scores. Support evaluation for different roles and add KL-divergence metric (see --infer_metrics).
• Jul 12,2020: We update the AirDialogue dataset to version v1.1. We fixed typos, misalignment between KB file and dialogue file. Please download and use the new data.

Prerequisites

General

• python (verified on 3.7)
• wget

Python Packages

• tensorflow (tested on 1.15.0)
• tqdm
• nltk
• flask (for visualization)

Install

To install the bleeding edge from github, use

python setup.py install

Quick Start

Scoring

The official scoring function evaluates the predictive results for a trained model and compare it to the AirDialogue dataset.

airdialogue score --true_data PATH_TO_DATA_FILE --true_kb PATH_TO_KB_FILE \
    --infer_metrics bleu

--infer_metrics can be one of (bleu:all|rouge:all|kl:all|bleu:brief|kl:brief). brief mode gives a single number metric. (bleu|kl) is equivalent to (belu:brief|kl:brief)

Context Generation

Context generator generates a valid context-action pair without conversatoin history.

airdialogue contextgen \
    --output_data PATH_TO_OUTPUT_DATA_FILE \
    --output_kb PATH_TO_OUTPUT_KB_FILE \
    --num_samples 100

Preprocessing

AirDialogue proprocess tookie tokenizes dialogue. Preprocess on AirDialogue data requires 50GB of ram to work. Parameter job_type is a set of 5 bits separted by |, which reqpresents train|eval|infer|sp-train|sp-eval. Parameter input_type can be either context for context only data or dialogue for dialogue data with full history.

airdialogue prepro \
  --data_file PATH_TO_DATA_FILE \
  --kb_file PATH_TO_KB_FILE \
  --output_dir "./data/airdialogue/" \
  --output_prefix 'train' --job_type '0|0|0|1|0' --input_type context

Simulator

Simulator is built on top of context generator that provides not only a context-action pair but also a full conversation history generated by two templated chatbot agents.

airdialogue sim \
    --output_data PATH_TO_OUTPUT_DATA_FILE \
    --output_kb PATH_TO_OUTPUT_KB_FILE \
    --num_samples 100

Visualization

Visualization tool displays the content of the raw json file.

airdialogue vis --data_path ./data/airdialogue/json/

Codalab simulator

To simulate running the Codalab selfplay workflow, you can run the following script that replicates the bundle workflow for the competition. This requires a model/scripts/codalab_selfplay_step.sh that can be run as

sh scripts/codalab_selfplay_step.sh out.txt data.json [kb.json]

More details can be found on the Airdialogue competition tutorial worksheet on Codalab.

bash airdialogue/codalab/simulate_codalab.sh <path_to_data>/json/dev_data.json <path_to_data>/json/dev_kb.json <model_folder>