AARGH!

End-to-end Retrieval-Generation for Task-Oriented Dialog

~ See the paper ~

 

---

This repository provides the code including the training and evaluation scripts associated with the paper AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog. It also contains the model weights of our best-performing Action-Aware Retrieval-Generative Hybrid model.

⌛ Installation

git clone https://github.com/Tomiinek/Aargh.git
cd Aargh
pip install -e .

💪 Training

We provide model weights of our best performing AARGH model with low and high blending parameter. You can download it here and here, respectively. The models should give Inform 82.8, Success: 71.2, and Inf: 90.3, Succ: 71.7 when evaluating in greedy mode on the test set.

0. What do you want to train?

It is possible to reproduce all model setups and experiments in the paper by selecting one of the following configurations: t5_joint, bert_dual_action, bert_dual, bert_poly, bert_action, or t5_vanilla. See the config directory for details. Use t5_joint to train the AARGH model.

seed="42"
model="t5_joint" 

1. Training of the AARGH model (or just the retrieval parts of the models)

out_path="${model}/retriever/${seed}"

python scripts/train.py \
    --deterministic \
    --gpus 1 \
    --num-workers 4
    --root-suffix ${out_path} \
    --config config/${model}.yaml \
    --set seed=${seed} \

2. Calculate train set embeddings using the retrieval parts

ckpt="last.ckpt"

ckpt_path="outputs/${model}/retriever/${seed}/checkpoints/${ckpt}"
out_path=$(dirname $(dirname "$ckpt_path"))

python scripts/get_embeddings.py ${ckpt_path} ${out_path} 64

3. Traning of the generative parts of the two stage models

# use this with on of `bert_dual_action`, `bert_dual`, `bert_poly`, and `bert_action` 

out_path="outputs/${model}/generator/${seed}"
hint_path="outputs/${model}/retriever/${seed}/hints.json"

python scripts/train.py \
    --deterministic \
    --gpus 1 \
    --num-workers 4
    --root-suffix ${out_path} \
    --config config/t5_separate.yaml \
    --set seed=${seed} hint_path=${hint_path} \

# use this with `t5_vanilla`

out_path="outputs/${model}/generator/${seed}"

python scripts/train.py \
    --deterministic \
    --gpus 1 \
    --num-workers 4
    --root-suffix ${out_path} \
    --config config/${model}.yaml \
    --set seed=${seed} \

🚀 Evaluation

1. Generate responses on test data

greedy="true" # or "false"
beam_size="8"
ckpt="last.ckpt"
fold="test" # or `val`

end_path=$([ "$model" = "t5_joint" ] && echo "retriever" || echo "generator")
out_path="outputs/${model}/${end_path}/${seed}"
ckpt_path="outputs/${model}/${end_path}/${seed}/checkpoints/${ckpt}"
hint_path="outputs/${model}/retriever/${seed}/train_encodings.pkl"
ret_path="outputs/${model}/retriever/${seed}/checkpoints/${ckpt}"

python generate.py \ 
    -c ${ckpt_path} \
    -g \
    -t context api_call \
    -o $out_path/${fold}_beam_outputs.json \
    -f $fold \
    --set \
        greedy=${greedy} \
        num_beams=${beam_size} \
        retrieval_checkpoint=${ret_path} \
        support_path=${hint_path}" 

2. Evaluate the generated responses

mode="beam" # or `greedy`
fold="test" # or `val`

end_path=$([ "$model" = "t5_joint" ] && echo "retriever" || echo "generator")

python scripts/get_responses_2.py \
    "outputs/${model}/${end_path}" ${fold} ${mode} \
    "outputs/${model}/${end_path}/${fold}_${mode}_dst_metrics_stats.txt

3. Evaluate the retrieval parts

fold="test" # or `val`
out_path="outputs/${model}/retriever"

python scripts/get_stats.py ${out_path} ${fold} "${out_path}/${fold}_action_accuracy_stats.txt"
python scrripts/get_responses.py ${out_path} ${fold} "${out_path}/${fold}_mwz_metrics_stats.txt"
python scripts/eval_clustering.py ${out_path} ${fold} "${out_path}/${fold}_clustering.txt "

💭 Citation

@inproceedings{nekvinda_aargh_2022,
	address = {Edinburgh, Scotland},
	title = {{AARGH}! {End}-to-end {Retrieval}-{Generation} for {Task}-{Oriented} {Dialog}},
	booktitle = {Proceedings of the {SIGdial} 2022 Conference},
	author = {Nekvinda, Tomáš and Dušek, Ondřej},
	month = sep,
	year = {2022},
	pages = {283--297},
}