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# coqa-baselines |
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We provide several baselines: conversational models, extractive reading comprehension models and their combined models for the [CoQA challenge](https://stanfordnlp.github.io/coqa/). See more details in the [paper](https://arxiv.org/abs/1808.07042). We also provide [instructions](codalab.md) on how to run pretrained models on Codalab -- our platform for evaluation on the test set. |
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As we use the [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) library for all our seq2seq experiments, please use the following command to clone our repository. |
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```bash |
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git clone --recurse-submodules git@github.com:stanfordnlp/coqa-baselines.git |
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``` |
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This code repository was mostly written by [Danqi Chen](https://github.com/danqi), built on top of the [DrQA](https://github.com/facebookresearch/DrQA) and [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) projects, with some help from [Shayne Longpre](https://github.com/Shayne13/) and [Siva Reddy](https://github.com/sivareddyg). If you have any questions about this repository, please use Github Issues. |
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## Requirements |
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``` |
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torch>=0.4.0 |
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torchtext==0.2.1 |
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gensim |
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pycorenlp |
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``` |
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## Download |
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Download the dataset: |
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```bash |
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mkdir data |
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wget -P data https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json |
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wget -P data https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json |
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``` |
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Download pre-trained word vectors: |
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```bash |
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mkdir wordvecs |
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wget -P wordvecs http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip |
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unzip -d wordvecs wordvecs/glove.42B.300d.zip |
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wget -P wordvecs http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip |
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unzip -d wordvecs wordvecs/glove.840B.300d.zip |
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``` |
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## Start a CoreNLP server |
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```bash |
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mkdir lib |
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wget -P lib http://central.maven.org/maven2/edu/stanford/nlp/stanford-corenlp/3.9.1/stanford-corenlp-3.9.1.jar |
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java -mx4g -cp lib/stanford-corenlp-3.9.1.jar edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000 |
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``` |
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## Conversational models |
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### Preprocessing |
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Generate the input files for seq2seq models --- needs to start a CoreNLP server (`n_history` can be changed to {0, 1, 2, ..} or -1): |
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```bash |
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python scripts/gen_seq2seq_data.py --data_file data/coqa-train-v1.0.json --n_history 2 --lower --output_file data/seq2seq-train-h2 |
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python scripts/gen_seq2seq_data.py --data_file data/coqa-dev-v1.0.json --n_history 2 --lower --output_file data/seq2seq-dev-h2 |
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``` |
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Preprocess the data and embeddings: |
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```bash |
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python seq2seq/preprocess.py -train_src data/seq2seq-train-h2-src.txt -train_tgt data/seq2seq-train-h2-tgt.txt -valid_src data/seq2seq-dev-h2-src.txt -valid_tgt data/seq2seq-dev-h2-tgt.txt -save_data data/seq2seq-h2 -lower -dynamic_dict -src_seq_length 10000 |
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PYTHONPATH=seq2seq python seq2seq/tools/embeddings_to_torch.py -emb_file_enc wordvecs/glove.42B.300d.txt -emb_file_dec wordvecs/glove.42B.300d.txt -dict_file data/seq2seq-h2.vocab.pt -output_file data/seq2seq-h2.embed |
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``` |
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### Training |
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Run a seq2seq (with attention) model: |
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```bash |
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python seq2seq/train.py -data data/seq2seq-h2 -save_model seq2seq_models/seq2seq -word_vec_size 300 -pre_word_vecs_enc data/seq2seq-h2.embed.enc.pt -pre_word_vecs_dec data/seq2seq-h2.embed.dec.pt -epochs 50 -gpuid 0 -seed 123 |
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``` |
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Run a seq2seq+copy model: |
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```bash |
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python seq2seq/train.py -data data/seq2seq-h2 -save_model seq2seq_models/seq2seq_copy -copy_attn -reuse_copy_attn -word_vec_size 300 -pre_word_vecs_enc data/seq2seq.embed.enc.pt -pre_word_vecs_dec data/seq2seq.embed.dec.pt -epochs 50 -gpuid 0 -seed 123 |
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``` |
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### Testing |
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```bash |
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python seq2seq/translate.py -model seq2seq_models/seq2seq_copy_acc_65.49_ppl_4.71_e15.pt -src data/seq2seq-dev-h2-src.txt -output seq2seq_models/pred.txt -replace_unk -verbose -gpu 0 |
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python scripts/gen_seq2seq_output.py --data_file data/coqa-dev-v1.0.json --pred_file seq2seq_models/pred.txt --output_file seq2seq_models/seq2seq_copy.prediction.json |
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``` |
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## Reading comprehension models |
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### Preprocessing |
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Generate the input files for the reading comprehension (extractive question answering) model -- needs to start a CoreNLP server: |
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```bash |
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python scripts/gen_drqa_data.py --data_file data/coqa-train-v1.0.json --output_file coqa.train.json |
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python scripts/gen_drqa_data.py --data_file data/coqa-dev-v1.0.json --output_file coqa.dev.json |
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``` |
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### Training |
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`n_history` can be changed to {0, 1, 2, ..} or -1. |
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```bash |
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python rc/main.py --trainset data/coqa.train.json --devset data/coqa.dev.json --n_history 2 --dir rc_models --embed_file wordvecs/glove.840B.300d.txt |
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``` |
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### Testing |
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```bash |
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python rc/main.py --testset data/coqa.dev.json --n_history 2 --pretrained rc_models |
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``` |
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## The pipeline model |
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### Preprocessing |
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```bash |
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python scripts/gen_pipeline_data.py --data_file data/coqa-train-v1.0.json --output_file1 data/coqa.train.pipeline.json --output_file2 data/seq2seq-train-pipeline |
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python scripts/gen_pipeline_data.py --data_file data/coqa-dev-v1.0.json --output_file1 data/coqa.dev.pipeline.json --output_file2 data/seq2seq-dev-pipeline |
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python seq2seq/preprocess.py -train_src data/seq2seq-train-pipeline-src.txt -train_tgt data/seq2seq-train-pipeline-tgt.txt -valid_src data/seq2seq-dev-pipeline-src.txt -valid_tgt data/seq2seq-dev-pipeline-tgt.txt -save_data data/seq2seq-pipeline -lower -dynamic_dict -src_seq_length 10000 |
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PYTHONPATH=seq2seq python seq2seq/tools/embeddings_to_torch.py -emb_file_enc wordvecs/glove.42B.300d.txt -emb_file_dec wordvecs/glove.42B.300d.txt -dict_file data/seq2seq-pipeline.vocab.pt -output_file data/seq2seq-pipeline.embed |
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``` |
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### Training |
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`n_history` can be changed to {0, 1, 2, ..} or -1. |
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```bash |
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python rc/main.py --trainset data/coqa.train.pipeline.json --devset data/coqa.dev.pipeline.json --n_history 2 --dir pipeline_models --embed_file wordvecs/glove.840B.300d.txt --predict_raw_text n |
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python seq2seq/train.py -data data/seq2seq-pipeline -save_model pipeline_models/seq2seq_copy -copy_attn -reuse_copy_attn -word_vec_size 300 -pre_word_vecs_enc data/seq2seq-pipeline.embed.enc.pt -pre_word_vecs_dec data/seq2seq-pipeline.embed.dec.pt -epochs 50 -gpuid 0 -seed 123 |
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``` |
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### Testing |
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```bash |
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python rc/main.py --testset data/coqa.dev.pipeline.json --n_history 2 --pretrained pipeline_models |
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python scripts/gen_pipeline_for_seq2seq.py --data_file data/coqa.dev.pipeline.json --output_file pipeline_models/pipeline-seq2seq-src.txt --pred_file pipeline_models/predictions.json |
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python seq2seq/translate.py -model pipeline_models/seq2seq_copy_acc_85.00_ppl_2.18_e16.pt -src pipeline_models/pipeline-seq2seq-src.txt -output pipeline_models/pred.txt -replace_unk -verbose -gpu 0 |
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python scripts/gen_seq2seq_output.py --data_file data/coqa-dev-v1.0.json --pred_file pipeline_models/pred.txt --output_file pipeline_models/pipeline.prediction.json |
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``` |
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## Results |
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All the results are based on `n_history = 2`: |
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| Model | Dev F1 | Dev EM | |
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| ------------- | ------------- | ------------- | |
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| seq2seq | 20.9 | 17.7 | |
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| seq2seq_copy | 45.2 | 38.0 | |
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| DrQA | 55.6 | 46.2 | |
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| pipeline | 65.0 | 54.9 | |
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## Citation |
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``` |
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@article{reddy2019coqa, |
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title={{CoQA}: A Conversational Question Answering Challenge}, |
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author={Reddy, Siva and Chen, Danqi and Manning, Christopher D}, |
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journal={Transactions of the Association of Computational Linguistics (TACL)}, |
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year={2019} |
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} |
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``` |
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## License |
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MIT |
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