KoCoSa: Korean Context-aware Sarcasm Detection Dataset

Core Concepts

Developing a context-aware sarcasm detection system for Korean dialogues is crucial, as shown by the creation of the KoCoSa dataset.

Abstract

The paper introduces the KoCoSa dataset for Korean sarcasm detection. Context is vital in detecting sarcasm, as demonstrated through examples. The dataset construction involved generating new dialogues using large language models and human annotation. Experimental results show the effectiveness of the dataset and baseline systems in sarcasm detection tasks. Ethical considerations were taken into account during data generation and annotation processes.

Stats

"KoCoSa consists of 12.8K daily Korean dialogues." "Our baseline system outperforms large language models like GPT-3.5 in Korean sarcasm detection." "The pre-trained Korean language model KLUE-RoBERTa significantly outperforms GPT-3.5."

Quotes

"We propose a comprehensive dataset generation pipeline for the context-aware sarcasm detection task." "Our baseline system outperforms strong baselines like large language models, such as GPT-3.5." "The main contributions of the paper can be summarized as follows."

Key Insights Distilled From

KoCoSa

by Yumin Kim,He... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2402.14428.pdf

Deeper Inquiries

How does cultural nuance impact sarcasm detection across different languages?

Cultural nuances play a significant role in sarcasm detection across different languages. Sarcasm is highly context-dependent and often relies on shared cultural knowledge, beliefs, and norms to be understood correctly. Different cultures may have varying levels of tolerance for sarcasm or use it in distinct ways. For example, what might be considered humorous sarcasm in one culture could be perceived as offensive or inappropriate in another. Additionally, the subtleties of language, such as tone of voice and non-verbal cues, can vary widely between cultures, further complicating the detection of sarcasm.

What are the implications of relying on translationese for studying sarcasm in various languages?

Relying solely on translationese when studying sarcasm in various languages can lead to inaccurate interpretations and misunderstandings. Translationese refers to translations that are overly literal and fail to capture the nuanced linguistic elements present in the original language. Since sarcasm is highly context-dependent and culturally influenced, direct translations may not convey the intended sarcastic meaning accurately. When studying sarcasm across multiple languages using translationese, researchers risk oversimplifying complex linguistic phenomena like irony and humor. This approach may overlook subtle cultural references or idiomatic expressions that are crucial for understanding sarcastic remarks accurately. As a result, relying on translationese alone can limit the effectiveness of cross-cultural studies on sarcasm detection.

How can context-aware approaches improve other natural language processing tasks beyond sarcasm detection?

Context-aware approaches have broad applications beyond just improving sarcasm detection in natural language processing tasks. By considering contextual information from previous dialogue turns or external sources, models can better understand ambiguous statements or infer implicit meanings within text data. Sentiment Analysis: Context-aware models can enhance sentiment analysis by capturing the emotional tone behind words based on surrounding text segments. Machine Translation: Incorporating context into machine translation systems helps generate more accurate translations by considering preceding sentences or paragraphs. Named Entity Recognition (NER): Contextual cues aid NER systems in identifying entities more effectively by analyzing their relationships with adjacent words. Question Answering: Contextual information enables question-answering models to provide more precise responses by understanding queries within their broader contexts. 5 .Text Summarization: By leveraging contextual clues from source documents, text summarization algorithms produce concise summaries that capture essential information accurately. Overall ,context-aware approaches enhance model performance across various NLP tasks by enabling them to leverage relevant information from surrounding text segments effectively while accounting for linguistic nuances specific to each task domain..