# Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering This model implements the work in the following paper: Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. [arXiv:1607.06275](https://arxiv.org/abs/1607.06275). If you use the dataset/code in your research, please cite the above paper: ```text @article{li:2016:arxiv, author = {Li, Peng and Li, Wei and He, Zhengyan and Wang, Xuguang and Cao, Ying and Zhou, Jie and Xu, Wei}, title = {Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering}, journal = {arXiv:1607.06275v2}, year = {2016}, url = {https://arxiv.org/abs/1607.06275v2}, } ``` ## Installation 1. Install PaddlePaddle v0.10.5 by the following commond. Note that v0.10.0 is not supported. ```bash # either one is OK # CPU pip install paddlepaddle # GPU pip install paddlepaddle-gpu ``` 2. Download the [WebQA](http://idl.baidu.com/WebQA.html) dataset by running ```bash cd data && ./download.sh && cd .. ``` ## Hyperparameters All the hyperparameters are defined in `config.py`. The default values are aligned with the paper. ## Training Training can be launched using the following command: ```bash PYTHONPATH=data/evaluation:$PYTHONPATH python train.py 2>&1 | tee train.log ``` ## Validation and Test WebQA provides two versions of validation and test sets. Automatic validation and test can be lauched by ```bash PYTHONPATH=data/evaluation:$PYTHONPATH python val_and_test.py models [ann|ir] ``` where * `models`: the directory where model files are stored. You can use `models` if `config.py` is not changed. * `ann`: using the validation and test sets with annotated evidence. * `ir`: using the validation and test sets with retrieved evidence. Note that validation and test can run simultaneously with training. `val_and_test.py` will handle the synchronization related problems. Intermediate results are stored in the directory `tmp`. You can delete them safely after validation and test. The results should be comparable with those shown in Table 3 in the paper. ## Inferring using a Trained Model Infer using a trained model by running: ```bash PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \ MODEL_FILE \ INPUT_DATA \ OUTPUT_FILE \ 2>&1 | tee infer.log ``` where * `MODEL_FILE`: a trained model produced by `train.py`. * `INPUT_DATA`: input data in the same format as the validation/test sets of the WebQA dataset. * `OUTPUT_FILE`: results in the format specified in the WebQA dataset for the evaluation scripts. ## Pre-trained Models We have provided two pre-trained models, one for the validation and test sets with annotated evidence, and one for those with retrieved evidence. These two models are selected according to the performance on the corresponding version of validation set, which is consistent with the paper. The models can be downloaded with ```bash cd pre-trained-models && ./download-models.sh && cd .. ``` The evaluation result on the test set with annotated evidence can be achieved by ```bash PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \ pre-trained-models/params_pass_00010.tar.gz \ data/data/test.ann.json.gz \ test.ann.output.txt.gz PYTHONPATH=data/evaluation:$PYTHONPATH \ python data/evaluation/evaluate-tagging-result.py \ test.ann.output.txt.gz \ data/data/test.ann.json.gz \ --fuzzy --schema BIO2 # The result should be # chunk_f1=0.739091 chunk_precision=0.686119 chunk_recall=0.800926 true_chunks=3024 result_chunks=3530 correct_chunks=2422 ``` And the evaluation result on the test set with retrieved evidence can be achieved by ```bash PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \ pre-trained-models/params_pass_00021.tar.gz \ data/data/test.ir.json.gz \ test.ir.output.txt.gz PYTHONPATH=data/evaluation:$PYTHONPATH \ python data/evaluation/evaluate-voting-result.py \ test.ir.output.txt.gz \ data/data/test.ir.json.gz \ --fuzzy --schema BIO2 # The result should be # chunk_f1=0.749358 chunk_precision=0.727868 chunk_recall=0.772156 true_chunks=3024 result_chunks=3208 correct_chunks=2335 ```