Papers by Kichang Yang
APEACH: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets (2022.findings-emnlp)
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| Challenge: | flaming or trolling in online communities is considered hostile behavior . a dataset of hate speech examples can be useful for detecting toxic or pejorative expressions . annotating on existing web text has several limitations that deter the dataset's reliability . |
| Approach: | They propose a dataset that asks users to generate hate speech examples followed by minimal post-labeling. |
| Outcome: | a new approach can collect useful datasets that are less sensitive to overlaps, the authors say . annotating on web text has several limitations that deter the dataset's reliability . |
A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)
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Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, Jihoon Kang
| Challenge: | Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications. |
| Approach: | They propose a framework for developing scalable, controllable, and reliable AI-driven agents that can be applied to real-world applications. |
| Outcome: | The proposed framework bridges the gap between academic research and real-world application, and enables scalable, controllable, and reliable AI-driven agents. |
Revisiting Modularized Multilingual NMT to Meet Industrial Demands (2020.emnlp-main)
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| Challenge: | Currently, the complete sharing of parameters for multilingual translation (1-1) is the most popular approach because of its compactness. |
| Approach: | They propose to use a multilingual neural machine translation model that only shares modules among the same languages as 1-1 to satisfy industrial requirements. |
| Outcome: | The proposed model can enjoy the benefits of multi-way training without the capacity bottleneck and low maintainability. |