Papers by Aditya Joshi
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English (2025.findings-acl)
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| Challenge: | despite large language models showing bias against non-mainstream varieties, there are no labeled datasets for sentiment analysis of English. |
| Approach: | They propose a benchmark for sentiment and sarcasm classification for three varieties of English . they manually annotate the datasets with sentiment and the sarcasmatic labels . |
| Outcome: | The proposed benchmark is based on a web-based content from Google Place reviews and Reddit comments. |
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection (P19-1)
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| Challenge: | Past work in personal health mention detection uses classification-based methods with human-engineered features or word embedding-based features. |
| Approach: | They propose to combine a pipeline-based and a feature augmentation-based approach to combine personal health mention detection with figurative usage detection to improve the accuracy of the prediction. |
| Outcome: | The proposed method improves the F-score of personal health mention detection by 2.21% over the pipeline-based approach and feature augmentation-based approaches. |
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study (2020.lrec-1)
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| Challenge: | Cross-domain sentiment analysis (CDSA) is a well-known problem in text analysis, but sufficient datasets may not be available for a domain to be trained. |
| Approach: | They propose to use 11 similarity metrics to facilitate cross-domain sentiment analysis to identify the best domains for CDSA for a given target domain. |
| Outcome: | The proposed approach performs better on 20 domain pairs and is validated by 11 similarity metrics. |
Predicting the Target Word of Game-playing Conversations using a Low-Rank Dialect Adapter for Decoder Models (2025.naacl-short)
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| Challenge: | Existing work proposes dialect adaptation for encoder models or encoder-decoder models. |
| Approach: | They propose to use MD-3 to combine task adapters and dialect adapters to decoder models using a masked word game-playing conversation. |
| Outcome: | The proposed architecture outperforms baselines on Indian English and Nigerian English on a masked conversation with two models. |
Sarcasm Target Identification: Dataset and An Introductory Approach (L18-1)
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| Challenge: | Past work on sarcasm detection has focused on identifying the sarcasm target of ridicule in a sarkastic text. |
| Approach: | They propose a task of extracting the sarcastic target of ridicule from a sarcastical text using a manually annotated dataset and an automatic approach. |
| Outcome: | The proposed approach establishes the viability of sarcasm target identification and will serve as a baseline for future work. |
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks (2022.findings-acl)
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| Challenge: | Inconsistency is observed in symmetric classification tasks that take two inputs and require the output to be invariant of the order of the inputs. |
| Approach: | They propose a consistency loss function to alleviate inconsistency in symmetric classification tasks that take two inputs and require the output to be invariant of the order of the inputs. |
| Outcome: | The proposed model improves consistency in predictions for three paraphrase detection datasets without significant drop in accuracy scores. |