AMR-SG

Code for our ACL Findings 2021 paper,

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

Weiwen Xu, Huihui Zhang, Deng Cai and Wai Lam.

Data Preparation

We present the results on OpenBookQA and ARC-Challenge in our paper. Due to the license issue, please directly download the datasets from their corresponding websites.

Data Annotation

1. We use this repo as our hypothesis generator and AMR-gs as our AMR parser. Please follow their instructions to annotate hypothesis and AMR for the datasets respectively.

   Once annotated, please organize the annotated files in the following directory (e.g. OpenBookQA)

   • Data/
     • obqa/
       • train.jsonl (train/dev/test original datasets)
       • dev.jsonl
       • test.jsonl
       • train-hypo.txt (train/dev/test hypotheses)
       • dev-hypo.txt
       • test-hypo.txt
       • train-amr.txt (train/dev/test AMRs)
       • dev-amr.txt
       • test-amr.txt
       • core-amr.txt (core fact AMRs from open-book)
       • comm-amr.txt (common fact AMRs from ARC-Corpus)

2. Create ElasticSearch Server

python data_preprocess/store_corpus.py --task_name ${input_file}

3. Data Preprocessing:

bash do_preprocess.sh ${input_file} (e.g. Data/obqa)

4. Retrieve Facts:

bash do_retrieve.sh ${input_file}

Training

bash do_finetune.sh

Citation

If you find this work useful, please star this repo and cite our paper as follows:

@inproceedings{xu-etal-2021-dynamic,
    title = "Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering",
    author = "Xu, Weiwen  and
      Zhang, Huihui  and
      Cai, Deng  and
      Lam, Wai",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.90",
    doi = "10.18653/v1/2021.findings-acl.90",
    pages = "1044--1056",
}