Seungwon Yoon


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Nonsense!: Quality Control via Two-Step Reason Selection for Annotating Local Acceptability and Related Attributes in News Editorials
Wonsuk Yang | Seungwon Yoon | Ada Carpenter | Jong Park
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Annotation quality control is a critical aspect for building reliable corpora through linguistic annotation. In this study, we present a simple but powerful quality control method using two-step reason selection. We gathered sentential annotations of local acceptance and three related attributes through a crowdsourcing platform. For each attribute, the reason for the choice of the attribute value is selected in a two-step manner. The options given for reason selection were designed to facilitate the detection of a nonsensical reason selection. We assume that a sentential annotation that contains a nonsensical reason is less reliable than the one without such reason. Our method, based solely on this assumption, is found to retain the annotations with satisfactory quality out of the entire annotations mixed with those of low quality.

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Computer Assisted Annotation of Tension Development in TED Talks through Crowdsourcing
Seungwon Yoon | Wonsuk Yang | Jong Park
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP

We propose a method of machine-assisted annotation for the identification of tension development, annotating whether the tension is increasing, decreasing, or staying unchanged. We use a neural network based prediction model, whose predicted results are given to the annotators as initial values for the options that they are asked to choose. By presenting such initial values to the annotators, the annotation task becomes an evaluation task where the annotators inspect whether or not the predicted results are correct. To demonstrate the effectiveness of our method, we performed the annotation task in both in-house and crowdsourced environments. For the crowdsourced environment, we compared the annotation results with and without our method of machine-assisted annotation. We find that the results with our method showed a higher agreement to the gold standard than those without, though our method had little effect at reducing the time for annotation. Our codes for the experiment are made publicly available.