Papers by Raksha Sharma
Late Fusion of Transformers for Sentiment Analysis of Code-Switched Data (2023.findings-emnlp)
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| Challenge: | Code-switching is a common phenomenon in multilingual communities . however, sentiment analysis of code-switch data is elusive and unexplored . |
| Approach: | They propose to combine two transformers using logits of their output and feed them to a neural network for sentiment analysis. |
| Outcome: | The proposed system achieves an F1 score of 73.66% for English-Hi and 61.24% for English . it outperforms the best model reported for the GLUECoS benchmark dataset. |
Power doesn’t reside in size: A Low Parameter Hybrid Language Model (HLM) for Sentiment Analysis in Code-mixed data (2025.emnlp-main)
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Pavan Sai Balaga, Nagasamudram Karthik, Challa Vishwanath, Raksha Sharma, Rudra Murthy, Ashish Mittal
| Challenge: | Code-mixed text presents significant challenges for machine learning due to interplay of distinct grammatical structures, effectively forming a hybrid language. |
| Approach: | They propose a Hybrid Language Model that combines a multilingual encoder and a lightweight decoder to achieve sentiment classification performance comparable to those of fine-tuned Large Language Models. |
| Outcome: | The proposed model outperforms models trained individually in sentiment detection tasks. |
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification (P18-1)
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| Challenge: | Cross-domain sentiment classification is challenging due to polarity orientation and significance differences . supervised learning algorithms have to be re-trained on every new domain . |
| Approach: | They propose that words that do not change their polarity and significance represent transferable information across domains for cross-domain sentiment classification. |
| Outcome: | The proposed method improves cross-domain sentiment classification performance by identifying polarity-preserving significant words across domains. |