This repository provides the implementation for the paper Combining Fact Extraction and Verification with Neural Semantic Matching Networks (AAAI 2019 and EMNLP-FEVER Shared Task Rank-1 System).
- Python 3.6
- pytorch 0.4.1
- allennlp 0.7.1
- sqlitedict
- wget
- flashtext
- pexpect
- fire
- inflection
Try to install the package as the order above. Previous version of pytorch can be find at legacy pytorch.
- Setup the python environment and download the required package listed above.
- Run the preparation script.
source setup.sh
bash ./scripts/prepare.shThe script will download all the required data, the auxiliary packages and files.
- Tokenize the dataset and build wiki document database for easy and fast access and query.
python src/pipeline/prepare_data.py tokenization # Tokenization
python src/pipeline/prepare_data.py build_database # Build document database. (This might take a while)After preparation, the following folder should contain similar files as listed below.
data
├── fever
│ ├── license.html
│ ├── shared_task_dev.jsonl
│ ├── shared_task_test.jsonl
│ └── train.jsonl
├── fever.db
├── id_dict.jsonl
├── license.html
├── sentence_tokens.json
├── tokenized_doc_id.json
├── tokenized_fever
│ ├── shared_task_dev.jsonl
│ └── train.jsonl
├── vocab_cache
│ └── nli_basic
│ ├── labels.txt
│ ├── non_padded_namespaces.txt
│ ├── tokens.txt
│ ├── unk_count_namespaces.txt
│ └── weights
│ └── glove.840B.300d
├── wiki-pages
│ ├── wiki-001.jsonl
│ ├── ... ...
│ └── wiki-109.jsonl
└── wn_feature_p
├── ant_dict
├── em_dict
├── em_lemmas_dict
├── hyper_lvl_dict
├── hypernym_stems_dict
├── hypo_lvl_dict
└── hyponym_stems_dictdep_packages
├── DrQA
└── stanford-corenlp-full-2017-06-09results
└── chaonan99saved_models
├── saved_nli_m
├── nn_doc_selector
└── saved_sselectorRunning the pipeline system on the dev set with the code below:
python src/pipeline/auto_pipeline.pyNote that this pipeline is the (SotA) model in the AAAI paper.
For EMNLP-FEVER Shared Task version, please refer to src/nli/mesim_wn_simi_v1_3.py and src/pipeline/pipeline_process.py.
If you find this implementation helpful, please consider citing:
@inproceedings{nie2019combining,
title={Combining Fact Extraction and Verification with Neural Semantic Matching Networks},
author={Yixin Nie and Haonan Chen and Mohit Bansal},
booktitle={Association for the Advancement of Artificial Intelligence ({AAAI})},
year={2019}
}