한국어 반어 표현 탐지기
Korean Ironic Expression Detector
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참고문헌
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초록
자연어 처리 분야에서 반어 및 비꼼 탐지의 중요성이 커지고 있음에도 불구하고, 한국어에 관한 연구는 다른 언어들에 비해 상대적으로 많이부족한 편이다. 본 연구는 한국어 텍스트에서의 반어 탐지를 위해 다양한 모델을 실험하는 것을 목적으로 한다. 본 연구는 BERT기반 모델인 KoBERT와 ChatGPT를 사용하여 반어 탐지 실험을 수행하였다. KoBERT의 경우, 감성 데이터를 추가 학습하는 두 가지 방법(전이 학습, 멀티태스크 학습)을적용하였다. 또한 ChatGPT의 경우, Few-Shot Learning기법을 적용하여 프롬프트에 입력되는 예시 문장의 개수를 증가시켜 실험하였다. 실험을수행한 결과, 감성 데이터를 추가학습한 전이 학습 모델과 멀티태스크 학습 모델이 감성 데이터를 추가 학습하지 않은 기본 모델보다 우수한 성능을보였다. 한편, ChatGPT는 KoBERT에 비해 현저히 낮은 성능을 나타내었으며, 입력 예시 문장의 개수를 증가시켜도 뚜렷한 성능 향상이 이루어지지않았다. 종합적으로, 본 연구는 KoBERT를 기반으로 한 모델이 ChatGPT보다 반어 탐지에 더 적합하다는 결론을 도출했으며, 감성 데이터의 추가학습이 반어 탐지 성능 향상에 기여할 수 있는 가능성을 제시하였다.
Abstract
Despite the increasing importance of irony and sarcasm detection in the field of natural language processing, research on the Koreanlanguage is relatively scarce compared to other languages. This study aims to experiment with various models for irony detection inKorean text. The study conducted irony detection experiments using KoBERT, a BERT-based model, and ChatGPT. For KoBERT, twomethods of additional training on sentiment data were applied (Transfer Learning and MultiTask Learning). Additionally, for ChatGPT,the Few-Shot Learning technique was applied by increasing the number of example sentences entered as prompts. The results of theexperiments showed that the Transfer Learning and MultiTask Learning models, which were trained with additional sentiment data,outperformed the baseline model without additional sentiment data. On the other hand, ChatGPT exhibited significantly lower performancecompared to KoBERT, and increasing the number of example sentences did not lead to a noticeable improvement in performance. Inconclusion, this study suggests that a model based on KoBERT is more suitable for irony detection than ChatG
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