Jinyoung Yeo


2020

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Less is More: Attention Supervision with Counterfactuals for Text Classification
Seungtaek Choi | Haeju Park | Jinyoung Yeo | Seung-won Hwang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We aim to leverage human and machine intelligence together for attention supervision. Specifically, we show that human annotation cost can be kept reasonably low, while its quality can be enhanced by machine self-supervision. Specifically, for this goal, we explore the advantage of counterfactual reasoning, over associative reasoning typically used in attention supervision. Our empirical results show that this machine-augmented human attention supervision is more effective than existing methods requiring a higher annotation cost, in text classification tasks, including sentiment analysis and news categorization.

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Label-Efficient Training for Next Response Selection
Seungtaek Choi | Myeongho Jeong | Jinyoung Yeo | Seung-won Hwang
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

This paper studies label augmentation for training dialogue response selection. The existing model is trained by “observational” annotation, where one observed response is annotated as gold. In this paper, we propose “counterfactual augmentation” of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.

2019

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Learning with Limited Data for Multilingual Reading Comprehension
Kyungjae Lee | Sunghyun Park | Hojae Han | Jinyoung Yeo | Seung-won Hwang | Juho Lee
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper studies the problem of supporting question answering in a new language with limited training resources. As an extreme scenario, when no such resource exists, one can (1) transfer labels from another language, and (2) generate labels from unlabeled data, using translator and automatic labeling function respectively. However, these approaches inevitably introduce noises to the training data, due to translation or generation errors, which require a judicious use of data with varying confidence. To address this challenge, we propose a weakly-supervised framework that quantifies such noises from automatically generated labels, to deemphasize or fix noisy data in training. On reading comprehension task, we demonstrate the effectiveness of our model on low-resource languages with varying similarity to English, namely, Korean and French.

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Soft Representation Learning for Sparse Transfer
Haeju Park | Jinyoung Yeo | Gengyu Wang | Seung-won Hwang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances. This paper argues that hard-parameter sharing, of hard-coding layers shared across different tasks or languages, cannot generalize well, when sharing with a loosely related task. Such case, which we call sparse transfer, might actually hurt performance, a phenomenon known as negative transfer. Our contribution is using adversarial training across tasks, to “soft-code” shared and private spaces, to avoid the shared space gets too sparse. In CLL, our proposed architecture considers another challenge of dealing with low-quality input.

2018

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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)