Stance Detection in Hinglish Data using the BART-large-MNLI Integration Model - Giddaluru, Somasekhar; Maturi, Sreerama Murty; Ooruchintala, Obulesu; Munirathnam, Mahendra (Engineering, Technology & Applied Science Research, 2024)
Real-time stance detection can be used in a wide range of applications like debate analysis, sentiment analysis, system feedback, etc. This study focuses on stance detection in political speeches, discerning whether the speaker is in favor, against, neutral, or lacking any stance on a given topic. The problem with this type of speeches dwells in the modification of the existing methods of stance detection to allow for the fine distinctions of Hinglish, a language mixture blending Hindi and English, as conveyed in human-edited texts. The proposed method utilizes the Bidirectional Auto-Regressive Transformers Multi-Genre Natural Language Inference (BART-large-MNLI) model with zero-Shot, few-Shot and N-Shot learning approaches. The proposed model is compared with the existing models of stance detection on Hinglish texts. For the pre-trained BART-based models, a limited number of labeled examples are utilized to determine the labels of test instances. For the other models, the train-test split method is adopted to get accurate results. The results indicate that the model surpasses the previous models.