Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.
QuAC is meant to be an academic resource and has significant limitations. Please read our detailed datasheet before considering it for any practical application.
No, QuAC shares many principles with SQuAD 2.0 such as span based evaluation and unanswerable questions (including website design principles! Big thanks for sharing the code!) but incorporates a new dialog component. We expect models can be easily evaluated on both resources and have tried to make our evaluation protocol as similar as possible to their own.
Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):
To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input. To run the evaluation, use
python scorer.py --val_file <path_to_val> --model_output <path_to_predictions> --o eval.json;.
Once you have a built a model that works to your expectations on the dev set, you submit it to get official scores on the dev and a hidden test set. To preserve the integrity of test results, we do not release the test set to the public. Instead, we require you to submit your model so that we can run it on the test set for you. The submission process is very similar to SQuaD 2.0 (Live!):Submission Tutorial
First, download the duck The DuckThen, put this macro in your latex:
Finally, enjoy the command \daffy in your paper!
There can be only one duck.
(Choi et al. EMNLP '18)
Sep 26, 2018
|FlowQA (single model)|
Allen Institute of AI
2Jan 30, 2019
|BERT + History Answer Embedding (single model)|
3Aug 20, 2018
|BiDAF++ w/ 2-Context (baseline)|
4Aug 20, 2018