Papers by Jasabanta Patro
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News (2021.eacl-main)
Copied to clipboard
| Challenge: | Exaggerations in health news can have tremendous adverse effects on the lifestyle of the common masses who feed themselves mostly on such news instead of the source scientific publication. |
| Approach: | They propose a three-step approach that extracts relation phrases from a scientific paper and then classifies the strength of the relationship phrase extracted. |
| Outcome: | The proposed approach outperforms baseline models that compare state-of-the-art embedding of the statement pairs through a binary classifier or recast the problem as a textual entailment task. |
Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection (2020.acl-main)
Copied to clipboard
| Challenge: | In this paper, we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. |
| Approach: | They propose to use code-switching patterns to improve various downstream NLP applications. |
| Outcome: | The proposed features can improve humour, sarcasm and hate speech detection tasks. |
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Specifically, we tried native sample mixing, multi-task learning, and prompting and instruction finetuning very large multilingual language models (VMLMs). |
| Approach: | They used native sample mixing, multi-task learning and prompting and instruction finetuning to improve code-mixed humour and sarcasm detection. |
| Outcome: | The proposed methods improve humour and sarcasm detection by adding native samples to training sets and multitask learning and prompting and instruction finetuning VMLMs. |
A deep-learning framework to detect sarcasm targets (D19-1)
Copied to clipboard
| Challenge: | Existing methods for sarcasm target detection are difficult to implement in natural language processing. |
| Approach: | They propose a deep learning framework for sarcasm target detection in predefined sarkastic texts. |
| Outcome: | The proposed framework improves accuracy and accuracy in match and dice scores compared to the current state-of-the-art framework. |