Papers by Chandresh Maurya
Indic-TEDST: Datasets and Baselines for Low-Resource Speech to Text Translation (2024.lrec-main)
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| Challenge: | Speech-to-text Translation (ST) tasks are performed by human translators with proficiency in both the source and target languages. |
| Approach: | a new study compares the performance of SOTA ST models on low-resource languages . the authors propose to use a dataset to compare the models on high-resourced languages based on the results of their research . |
| Outcome: | a new study shows that only a few models have performed well on low-resource languages . the results indicate the need for specialized models for low- and high-resourced languages based on the dataset . |
Benchmark Creation for Aspect-Based Sentiment Analysis in Low-Resource Odia Language and Evaluation through Fine-Tuning of Multilingual Models (2025.coling-main)
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| Challenge: | Aspect-based sentiment analysis is underexplored in low-resource languages such as Odia . a dataset is annotated for two tasks: Aspect Term Extraction (ATE) and Aspect Polarity Classification (APC) |
| Approach: | They propose to use a dataset for aspect-based sentiment analysis in Odia . they use ensemble data augmentation and a fine-tuned paraphrase generation model . |
| Outcome: | The proposed dataset is annotated for two tasks: ATE and APC . the proposed dataset will spur more work for the ABSA task in Odia . |