Ji-Ung Lee


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TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation
Lorenz Stangier | Ji-Ung Lee | Yuxi Wang | Marvin Müller | Nicholas Frick | Joachim Metternich | Iryna Gurevych
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

Collecting and annotating task-oriented dialog data is difficult, especially for highly specific domains that require expert knowledge. At the same time, informal communication channels such as instant messengers are increasingly being used at work. This has led to a lot of work-relevant information that is disseminated through those channels and needs to be post-processed manually by the employees. To alleviate this problem, we present TexPrax, a messaging system to collect and annotate _problems_, _causes_, and _solutions_ that occur in work-related chats. TexPrax uses a chatbot to directly engage the employees to provide lightweight annotations on their conversation and ease their documentation work. To comply with data privacy and security regulations, we use an end-to-end message encryption and give our users full control over their data which has various advantages over conventional annotation tools. We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work. Overall, we collect 202 task-oriented German dialogues containing 1,027 sentences with sentence-level expert annotations. Our data analysis also reveals that real-world conversations frequently contain instances with code-switching, varying abbreviations for the same entity, and dialects which NLP systems should be able to handle.

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Annotation Curricula to Implicitly Train Non-Expert Annotators
Ji-Ung Lee | Jan-Christoph Klie | Iryna Gurevych
Computational Linguistics, Volume 48, Issue 2 - June 2022

Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally taxing, and induce errors into the resulting annotations; especially in citizen science or crowdsourcing scenarios where domain expertise is not required. To alleviate these issues, this work proposes annotation curricula, a novel approach to implicitly train annotators. The goal is to gradually introduce annotators into the task by ordering instances to be annotated according to a learning curriculum. To do so, this work formalizes annotation curricula for sentence- and paragraph-level annotation tasks, defines an ordering strategy, and identifies well-performing heuristics and interactively trained models on three existing English datasets. Finally, we provide a proof of concept for annotation curricula in a carefully designed user study with 40 voluntary participants who are asked to identify the most fitting misconception for English tweets about the Covid-19 pandemic. The results indicate that using a simple heuristic to order instances can already significantly reduce the total annotation time while preserving a high annotation quality. Annotation curricula thus can be a promising research direction to improve data collection. To facilitate future research—for instance, to adapt annotation curricula to specific tasks and expert annotation scenarios—all code and data from the user study consisting of 2,400 annotations is made available.1


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Proceedings of the First Workshop on Interactive Learning for Natural Language Processing
Kianté Brantley | Soham Dan | Iryna Gurevych | Ji-Ung Lee | Filip Radlinski | Hinrich Schütze | Edwin Simpson | Lili Yu
Proceedings of the First Workshop on Interactive Learning for Natural Language Processing

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Investigating label suggestions for opinion mining in German Covid-19 social media
Tilman Beck | Ji-Ung Lee | Christina Viehmann | Marcus Maurer | Oliver Quiring | Iryna Gurevych
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)

This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement – in terms of inter-annotator agreement (+.14 Fleiss’ κ) and annotation quality – compared to students that do not receive any label suggestions. We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.


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Substituto – A Synchronous Educational Language Game for Simultaneous Teaching and Crowdsourcing
Marianne Grace Araneta | Gülşen Eryiğit | Alexander König | Ji-Ung Lee | Ana Luís | Verena Lyding | Lionel Nicolas | Christos Rodosthenous | Federico Sangati
Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning

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Empowering Active Learning to Jointly Optimize System and User Demands
Ji-Ung Lee | Christian M. Meyer | Iryna Gurevych
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading. In this paper, we propose a new active learning approach that jointly optimizes the seemingly counteracting objectives of the active learning system (training efficiently) and the user (receiving useful instances). We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user, while the users should receive only exercises that match their skills. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.


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Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Steffen Eger | Gözde Gül Şahin | Andreas Rücklé | Ji-Ung Lee | Claudia Schulz | Mohsen Mesgar | Krishnkant Swarnkar | Edwin Simpson | Iryna Gurevych
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., “!d10t”) or as a writing style (“1337” in “leet speak”), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual perturbations demonstrate. We investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82%. We then explore three shielding methods—visual character embeddings, adversarial training, and rule-based recovery—which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.

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Manipulating the Difficulty of C-Tests
Ji-Ung Lee | Erik Schwan | Christian M. Meyer
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically. This is a crucial step towards generating learner-adaptive exercises for self-directed language learning and preparing language assessment tests. To reach the desired difficulty level, we manipulate the size and the distribution of gaps based on absolute and relative gap difficulty predictions. We evaluate our approach in corpus-based experiments and in a user study with 60 participants. We find that both strategies are able to generate C-tests with the desired difficulty level.