HyunJeong Choe


2023

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PRESTO: A Multilingual Dataset for Parsing Realistic Task-Oriented Dialogs
Rahul Goel | Waleed Ammar | Aditya Gupta | Siddharth Vashishtha | Motoki Sano | Faiz Surani | Max Chang | HyunJeong Choe | David Greene | Chuan He | Rattima Nitisaroj | Anna Trukhina | Shachi Paul | Pararth Shah | Rushin Shah | Zhou Yu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Research interest in task-oriented dialogs has increased as systems such as Google Assistant, Alexa and Siri have become ubiquitous in everyday life. However, the impact of academic research in this area has been limited by the lack of datasets that realistically capture the wide array of user pain points. To enable research on some of the more challenging aspects of parsing realistic conversations, we introduce PRESTO, a public dataset of over 550K contextual multilingual conversations between humans and virtual assistants. PRESTO contains a diverse array of challenges that occur in real-world NLU tasks such as disfluencies, code-switching, and revisions. It is the only large scale human generated conversational parsing dataset that provides structured context such as a user’s contacts and lists for each example. Our mT5 model based baselines demonstrate that the conversational phenomenon present in PRESTO are challenging to model, which is further pronounced in a low-resource setup.

2016

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Consistent Word Segmentation, Part-of-Speech Tagging and Dependency Labelling Annotation for Chinese Language
Mo Shen | Wingmui Li | HyunJeong Choe | Chenhui Chu | Daisuke Kawahara | Sadao Kurohashi
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this paper, we propose a new annotation approach to Chinese word segmentation, part-of-speech (POS) tagging and dependency labelling that aims to overcome the two major issues in traditional morphology-based annotation: Inconsistency and data sparsity. We re-annotate the Penn Chinese Treebank 5.0 (CTB5) and demonstrate the advantages of this approach compared to the original CTB5 annotation through word segmentation, POS tagging and machine translation experiments.

2014

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A Database for Measuring Linguistic Information Content
Richard Sproat | Bruno Cartoni | HyunJeong Choe | David Huynh | Linne Ha | Ravindran Rajakumar | Evelyn Wenzel-Grondie
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Which languages convey the most information in a given amount of space? This is a question often asked of linguists, especially by engineers who often have some information theoretic measure of “information” in mind, but rarely define exactly how they would measure that information. The question is, in fact remarkably hard to answer, and many linguists consider it unanswerable. But it is a question that seems as if it ought to have an answer. If one had a database of close translations between a set of typologically diverse languages, with detailed marking of morphosyntactic and morphosemantic features, one could hope to quantify the differences between how these different languages convey information. Since no appropriate database exists we decided to construct one. The purpose of this paper is to present our work on the database, along with some preliminary results. We plan to release the dataset once complete.