Stance Detection in Hinglish Data using the BART-large-MNLI Integration Model
Received: 4 May 2024 | Revised: 16 May 2024 | Accepted: 21 May 2024 | Online: 26 June 2024
Corresponding author: Somasekhar Giddaluru
Abstract
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.
Keywords:
stance detection, analysis, BART, HinglishDownloads
References
Y. Du, M. Shi, F. Wei, and G. Li, "Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations." arXiv, Jul. 18, 2023.
G. K. Nayak, R. Rawal, I. Khatri, and A. Chakraborty, "Robust Few-shot Learning Without Using any Adversarial Samples." arXiv, Nov. 03, 2022.
W. Li et al., "LibFewShot: A Comprehensive Library for Few-Shot Learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 14938–14955, Dec. 2023.
B. Xu, Z. Zeng, C. Lian, and Z. Ding, "Generative Mixup Networks for Zero-Shot Learning," IEEE Transactions on Neural Networks and Learning Systems, pp. 1–12, 2022.
N. Lai, M. Kan, C. Han, X. Song, and S. Shan, "Learning to Learn Adaptive Classifier–Predictor for Few-Shot Learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 8, pp. 3458–3470, Aug. 2021.
A. Naghizadeh, D. N. Metaxas, and D. Liu, "Greedy auto-augmentation for n-shot learning using deep neural networks," Neural Networks, vol. 135, pp. 68–77, Mar. 2021.
S. R. Sane, S. Tripathi, K. R. Sane, and R. Mamidi, "Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning," in Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Minneapolis, MN, USA, Mar. 2019, pp. 1–5.
T. T. Sasidhar, B. Premjith, and K. P. Soman, "Emotion Detection in Hinglish(Hindi+English) Code-Mixed Social Media Text," Procedia Computer Science, vol. 171, pp. 1346–1352, Jan. 2020.
M. Hardalov, A. Arora, P. Nakov, and I. Augenstein, "Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 10, pp. 10729–10737, Jun. 2022.
H. Wen and A. Hauptmann, "Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation," in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Toronto, Canada, Apr. 2023, pp. 1491–1499.
E. Allaway and K. McKeown, "Zero-shot stance detection: Paradigms and challenges," Frontiers in Artificial Intelligence, vol. 5, Jan. 2023.
V. Barriere and A. Balahur, "Multilingual Multi-Target Stance Recognition in Online Public Consultations," Mathematics, vol. 11, no. 9, Jan. 2023, Art. no. 2161.
S. Mishra et al., "Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes." arXiv, Sep. 12, 2023.
A. Shahade, K. Walse, V. M. Thakare, and M. Atique, "Multi-lingual opinion mining for social media discourses: an approach using deep learning based hybrid fine-tuned smith algorithm with adam optimizer," International Journal of Information Management Data Insights, vol. 3, Nov. 2023, Art. no. 100182.
Z. Yao, W. Yang, and F. Wei, "Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning," Entropy, vol. 26, no. 4, Apr. 2024, Art. no. 325.
A. K. Barman, J. Sarmah, S. Basumatary, and A. Nag, "Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12581–12586, Feb. 2024.
A. Kumar, R. Sharma, and P. Bedi, "Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14219–14224, Jun. 2024.
D. Chopra, N. Joshi, and I. Mathur, "A Review on Machine Translation in Indian Languages," Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3475–3478, Oct. 2018.
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Copyright (c) 2024 Somasekhar Giddaluru, Sreerama Murty Maturi, Obulesu Ooruchintala, Mahendra Munirathnam
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