A Query Reformulation Framework based on Deep Reinforcement Learning.
The datasets and auxiliary files can be downloaded here:
- msa_dataset.hdf5: MS Academic dataset: a query is the title of a paper and the ground-truth documents are the papers cited within.
- msa_corpus.hdf5: MS Academic corpus: each document consists of a paper title and abstract.
- jeopardy_dataset.hdf5: Jeopardy dataset: queries are Jeopardy! TV Show questions and answers are the Wikipedia articles whose title is the answer.
- jeopardy_corpus.hdf5: Jeopardy Corpus: All the English Wikipedia Articles (5.9M documents).
- trec-car_dataset.hdf5: TREC-CAR dataset: a query is Wikipedia article title + a section within that article. Ground-truth documents are paragraphs within that section.
- trec-car_corpus.hdf5: TREC-CAR Corpus: Half of the English Wikipedia Paragraphs, except abstracts.
- D_cbow_pdw_8B_norm.pkl: A python dictionary containing 374,000 pretrained word embeddings from the Word2Vec tool.
The datasets are stored in the HDF5 format.
We provide wrapper classes to access them: dataset_hdf5.py and corpus_hdf5.py
The queries and documents can be accessed using the Python code below (h5py package is required):
#get training, validation and test lists of queries and relevant documents:
import dataset_hdf5
dt = dataset_hdf5.DatasetHDF5('path/to/the/dataset.hdf5')
queries_train, queries_valid, queries_test = dt.get_queries()
doc_titles_train, doc_titles_valid, doc_titles_test = dt.get_doc_ids()
# iterate over all documents in the corpus:
import corpus_hdf5
cp = corpus_hdf5.CorpusHDF5('path/to/the/corpus.hdf5')
for i, text in enumerate(cp.get_text_iter()):
print 'text:', text
print 'title:', cp.get_article_title(i)
After changing the properties in the parameters.py file to point to your local paths, the model can be trained using the following command:
THEANO_FLAGS='floatX=float32' python run.py
If you want to use a GPU:
THEANO_FLAGS='floatX=float32,device=gpu0' python run.py
Each minibatch iteration should take approximately 1 second on a K80 GPU. It should take 800,000 iterations (7-10 days) to reach a Recall@40 of 47.6% in the TREC-CAR dataset. It is normal that the model starts to select terms only after iteration 50,000.
To run the code, you will need:
We recommend that you have at least 32GB of RAM. If you are going to use a GPU, the card must have at least 6GB.
If you use this code as part of any published research, please acknowledge the following paper:
@article{nogueira2016task,
title={Task-Oriented Query Reformulation with Reinforcement Learning},
author={Nogueira, Rodrigo and Cho, Kyunghyun},
journal={arXiv preprint arXiv:1704.04572},
year={2017}
}
Copyright (c) 2017, Rodrigo Nogueira and Kyunghyun Cho
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