Indonesia

The research for detecting offensive language on online platforms has much advanced. However, the majority of these studies have primarily focused on English. Given the unique characteristics of offensive language, where social and cultural contexts significantly influence content understanding, language-specific datasets are essential. Acquiring comprehensive datasets in Korean, a less-resourced language, has mostly relied on human annotations, suffering from inherent limitations in terms of labor intensity and potential annotator bias. Automatic generation of datasets using generative methods offers an alternative approach to address these limitations, yet faces challenges in capturing linguistic and cultural diversities while maintaining native-level fluency. To address these challenges, we introduce a prompt design methodology, Korean Offensive language Machine Generation (K-OMG), using large language models. By manipulating three prompt factors, we find an effective prompt design to generate culturally aligned offensive language with fluent expressions. Experimental results demonstrate the high quality and utility of our automatically generated dataset. Our detailed analysis shows that the proposed approach achieves exceptional fluency in generating texts while effectively incorporating social and cultural diversities.

IJCNLP-AACL 2023

Generation of Korean Offensive Language by Leveraging Large Language Models via Prompt Design

technical paper

### Welcome to IJCNLP-AACL 2023!
I am honored and happy to welcome you to IJCNLP-AACL 2023! The conference hosts both the 13th
International Joint Conference on Natural Language Processing (IJCNLP) and the 3rd Conference of
the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL). 
The joint conferences of IJCNLP and AACL have been organized by alternating leadership in the
Asia-Pacific region, in odd years by Asian Federation of Natural Language Processing (AFNLP) and
in even years by AACL. The conference is organized by AFNLP this year. 

Considering the current situation that we are not yet fully over the coronavirus pandemic, the
conference is offered in a hybrid mode, off-line in Nusa Dua, Bali, Indonesia, and on-line with the help
of Underline, during November 1-4, 2023. For those attending off-line, I hope that you will find time to
explore the exciting local area, which remains quite warm and pleasant at this time of the year. For
those attending on-line, I hope that the attendance is not much hampered, sometimes even enhanced
and comfortable, though the on-line arrangements have been made without ample time. 

Message from Jong C. Park, Korea Advanced Institute of Science and Technology (KAIST) 
IJCNLP-AACL 2023 General Chair 
###### [Conference Handbook](https://drive.google.com/file/d/1S2JSXa105xJxIkNBfhYmGH20Om6wARBx/view?usp=sharing)

In order to acces this event page, you need to register [here](http://www.ijcnlp-aacl2023.org/)

The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics

Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal relations are fundamental to human decision making and reasoning, and extracting them from natural language texts is crucial for building effective natural language understanding models. However, the scarcity of annotated corpora for causal relations poses a challenge in the development of such tools. Thus, we created Resource for Extracting Cause, Effect, and Signal Spans (RECESS), a comprehensive corpus annotated for causality at different levels, including Cause, Effect, and Signal spans. The corpus contains 3,767 sentences, of which, 1,982 are causal sentences that contain a total of 2,754 causal relations. We report baseline experiments on two natural language tasks (Causal Sentence Classification, and Cause-Effect-Signal Span Detection), and establish initial benchmarks for future work. We conduct an in-depth analysis of the corpus and the properties of causal relations in text. RECESS is a valuable resource for developing and evaluating causal relation extraction models, benefiting researchers working on topics from information retrieval to natural language understanding and inference.

RECESS: Resource for Extracting Cause, Effect, and Signal Spans

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer. We construct FollowupQG, a dataset of over 3K real-world (initial question, answer, follow-up question) tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions. 

In contrast to existing datasets, questions in FollowupQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills (such as applying and relating). We evaluate current question generation models on their efficacy for generating follow-up questions, exploring how to generate specific types of follow-up questions based on step-by-step demonstrations. Our results validate FollowupQG as a challenging benchmark, as model-generated questions are adequate but far from human-raised questions in terms of informativeness and complexity.

FollowupQG: Towards information-seeking follow-up question generation

Generation of Korean Offensive Language by Leveraging Large Language Models via Prompt Design

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RECESS: Resource for Extracting Cause, Effect, and Signal Spans

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Generation of Korean Offensive Language by Leveraging Large Language Models via Prompt Design

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Next from IJCNLP-AACL 2023

RECESS: Resource for Extracting Cause, Effect, and Signal Spans

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads