Here are the dataset and codes for the Findings of NAACL paper titled "Learning to Execute Actions or Ask Clarification Questions".

python==3.7
torch==1.7
tensorboardX
prettytable

An intelligent agent should not only understand and execute the instructor's requests but also be able to take initiatives, e.g., asking clarification questions, in case the instructions are ambiguous.

A simple example of builder task.

• CollaborativeBuilding: Codes for collaborative building task;
• LearnToAsk: Codes for learning to ask task and joint learning task;
• builder_utterance_labels.json: Annotations of all builder utterances. Please ignore builder_utterance_labels.txt, which is our draft version.
• The raw dataset Minecraft Dialogue Corpus is from the repository.

Please download the original dataset. Then

unzip data.zip
cd data
wget https://nlp.stanford.edu/data/glove.42B.300d.zip
unzip glove.42B.300d.zip
cd ../CollaborativeBuilding/builder
python vocab.py --lower --use_speaker_tokens --oov_as_unk --all_splits --add_builder_utterances
cd ../..

Please run codes in CollaborativeBuilding and LearnToAsk for Collaborative Building task and Learning to Ask task respectively.

Please cite our work if it is helpful.

@inproceedings{Shi2022learning,
title = {Learning to Execute Actions or Ask Clarification Questions},
author = {Shi, Zhengxiang and Feng, Yue and Lipani, Aldo},
year = {2022},
address = {Seattle, Washington, USA},
booktitle = {Findings of the North American Chapter of the Association for Computational Linguistics},
publisher = {Association for Computational Linguistics},
keywords = {Conversational System, Clarification Questions},
url = {https://arxiv.org/abs/2204.08373}
}