A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC

September 27, 2018 · Entered Twilight · 🏛 North American Chapter of the Association for Computational Linguistics

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Repo contents: README.md, __init__.py, convert, datasets, evals, qualitative, visualizations, visualize

Authors Mark Yatskar arXiv ID 1809.10735 Category cs.CL: Computation & Language Citations 102 Venue North American Chapter of the Association for Computational Linguistics Repository https://github.com/my89/co-squac ⭐ 24 Last Checked 1 month ago

Abstract

We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers. We show that the datasets provide complementary coverage of the first two aspects, but weak coverage of the third. Because of the datasets' structural similarity, a single extractive model can be easily adapted to any of the datasets and we show improved baseline results on both SQuAD 2.0 and CoQA. Despite the similarity, models trained on one dataset are ineffective on another dataset, but we find moderate performance improvement through pretraining. To encourage cross-evaluation, we release code for conversion between datasets at https://github.com/my89/co-squac .