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This repository provides the implementation for the paper "Combining Fact Extraction and Verification withNeural Semantic Matching Networks".
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README.md

Combine-FEVER-NSMN

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).

Requirement

  • 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.

Preparation

  1. Setup the python environment and download the required package listed above.
  2. Run the preparation script.
source setup.sh
bash ./scripts/prepare.sh

The script will download all the required data, the auxiliary packages and files.

  1. 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_dict
dep_packages
├── DrQA
└── stanford-corenlp-full-2017-06-09
results
└── chaonan99
saved_models
├── saved_nli_m
├── nn_doc_selector
└── saved_sselector

Automatic pipeline procedure.

Running the pipeline system on the dev set with the code below:

python src/pipeline/auto_pipeline.py

Note 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.

Citation

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}
}
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