TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

This repo contains code for the paper Mandar Joshi, Eunsol Choi, Daniel Weld, Luke Zettlemoyer.

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension In Association for Computational Linguistics (ACL) 2017, Vancouver, Canada.

The data can be downloaded from the TriviaQA website. The Apache 2.0 License applies to both the code and the data.
Please contact Mandar Joshi (<first-name>90@cs.washington.edu) for suggestions and comments.

Requirements

General

Python 3. You should be able to run the evaluation scripts using Python 2.7 if you take care of unicode in utils.utils.py.
BiDAF requires Python 3 -- check the original repository for more details.

Python Packages

tensorflow (only if you want to run BiDAF, verified on r0.11)
nltk
tqdm

Evaluation

The dataset file parameter refers to files in the qa directory of the data (e.g., wikipedia-dev.json). For file format, check out the sample directory in the repo.

python3 -m evaluation.triviaqa_evaluation --dataset_file samples/triviaqa_sample.json --prediction_file samples/sample_predictions.json

Miscellaneous

If you have a SQuAD model and want to run on TriviaQA, please refer to utils.convert_to_squad_format.py