Papers by Won Ik Cho

10 papers
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives (2020.findings-emnlp)

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Challenge: Modern dialog managers face the challenge of having to fulfill human-level conversational skills as part of common user expectations.
Approach: They propose to extract the intent argument of non-canonical directives in a natural language format and build a parallel corpus for this purpose.
Outcome: The proposed method extracts the intent argument of non-canonical directives in a natural language format, which may yield more accurate parsing.
Discourse Component to Sentence (DC2S): An Efficient Human-Aided Construction of Paraphrase and Sentence Similarity Dataset (2020.lrec-1)

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Challenge: a dataset of similar sentences and paraphrases is a challenging task, but it requires high resources.
Approach: They propose a discourse component-based paraphrase generation scheme for directive utterances . they use Korean language, which has diverse paraphrasing due to frequent subject drop .
Outcome: The proposed scheme is efficient in terms of human-aided construction and content preservation.
Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (2022.findings-aacl)

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Challenge: Existing studies on how people interact with conversational agents have not investigated the interaction authenticity of human-like agents.
Approach: They construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting.
Outcome: The proposed taxonomy can be used for future research and industrial development.
AMAN: Agent for Mentoring and Assisting Newbies in MMORPG (2025.coling-industry)

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Challenge: AMAN is a chatbot designed to help novice gamers learn the gameplay mechanics of online games.
Approach: They propose a model that functions as a human-like chat buddy that interacts with users in a friendly manner while providing substantive informational depth.
Outcome: The proposed model integrates continual pre-training with a sequence of online resources and instruction tuning on curated dialogues.
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 .
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands (2022.lrec-1)

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Challenge: Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner.
Approach: They propose a corpus construction scheme that considers intent and formality of directives in Korean language.
Outcome: The proposed method is validated by a corpus construction scheme on Korean topics.
Modeling the Influence of Verb Aspect on the Activation of Typical Event Locations with BERT (2021.findings-acl)

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Challenge: Prior studies have shown that aspect of the main verb plays an important role in non-core semantic roles such as locations.
Approach: They tested the popular language model BERT to determine whether its predictions of prototypical locations were influenced by aspect.
Outcome: The language model BERT modelled the typicality of locations independently of the verb aspect.
Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)

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Challenge: Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text.
Approach: They propose a pipeline that expands existing evaluation sets by adding alternative valid terms for aspect and opinion.
Outcome: The proposed evaluation set uncovers the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by the single-answer GT sets.
Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study of LLM Hallucination on North Korea (2025.coling-main)

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Challenge: Existing solutions to hallucination in large language models (LLMs) focus on aligning models with credible sources or improving how models communicate their confidence in outputs.
Approach: They examine how best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests.
Outcome: The best-performing models generate information in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China).
OpenKorPOS: Democratizing Korean Tokenization with Voting-Based Open Corpus Annotation (2022.lrec-1)

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Challenge: Korean uses spaces at larger-than-word boundaries, unlike other East-Asian languages.
Approach: They propose to use Korean morphological analyzers to provide a sequence of morpheme-level tokens, losing information in the tokenization process.
Outcome: The proposed scheme improves existing tagging scheme and makes it friendlier to generative tasks.

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