SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0

no code implementations • LREC 2020 • Gyeongbok Lee, Seung-won Hwang, Hyunsouk Cho

Existing machine reading comprehension models are reported to be brittle for adversarially perturbed questions when optimizing only for accuracy, which led to the creation of new reading comprehension benchmarks, such as SQuAD 2. 0 which contains such type of questions.

Machine Reading Comprehension

QADiver: Interactive Framework for Diagnosing QA Models

no code implementations • 1 Dec 2018 • Gyeongbok Lee, Sungdong Kim, Seung-won Hwang

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset.

Question Answering

Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning

1 code implementation • LREC 2018 • Jinyoung Yeo, Gyeongbok Lee, Gengyu Wang, Seungtaek Choi, Hyunsouk Cho, Reinald Kim Amplayo, Seung-won Hwang

Commonsense Causal Reasoning Image Captioning +4