Hyunsouk Cho


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Self-Supervised Multimodal Opinion Summarization
Jinbae Im | Moonki Kim | Hoyeop Lee | Hyunsouk Cho | Sehee Chung
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder–decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.


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SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.0
Gyeongbok Lee | Seung-won Hwang | Hyunsouk Cho
Proceedings of the 12th Language Resources and Evaluation Conference

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. However, despite the super-human accuracy of existing models on such datasets, it is still unclear how the model predicts the answerability of the question, potentially due to the absence of a shared annotation for the explanation. To address such absence, we release SQuAD2-CR dataset, which contains annotations on unanswerable questions from the SQuAD 2.0 dataset, to enable an explanatory analysis of the model prediction. Specifically, we annotate (1) explanation on why the most plausible answer span cannot be the answer and (2) which part of the question causes unanswerability. We share intuitions and experimental results that how this dataset can be used to analyze and improve the interpretability of existing reading comprehension model behavior.


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Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning
Jinyoung Yeo | Gyeongbok Lee | Gengyu Wang | Seungtaek Choi | Hyunsouk Cho | Reinald Kim Amplayo | Seung-won Hwang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)