Aditya Shah


2023

pdf bib
ADEPT: Adapter-based Efficient Prompt Tuning Approach for Language Models
Aditya Shah | Surendrabikram Thapa | Aneesh Jain | Lifu Huang
Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

2022

pdf bib
A Multi-Modal Dataset for Hate Speech Detection on Social Media: Case-study of Russia-Ukraine Conflict
Surendrabikram Thapa | Aditya Shah | Farhan Jafri | Usman Naseem | Imran Razzak
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)

This paper presents a new multi-modal dataset for identifying hateful content on social media, consisting of 5,680 text-image pairs collected from Twitter, labeled across two labels. Experimental analysis of the presented dataset has shown that understanding both modalities is essential for detecting these techniques. It is confirmed in our experiments with several state-of-the-art multi-modal models. In future work, we plan to extend the dataset in size. We further plan to develop new multi-modal models tailored explicitly to hate-speech detection, aiming for a deeper understanding of the text and image relation. It would also be interesting to perform experiments in a direction that explores what social entities the given hate speech tweet targets.

2021

pdf bib
How effective is incongruity? Implications for code-mixed sarcasm detection
Aditya Shah | Chandresh Maurya
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

The presence of sarcasm in conversational systems and social media like chatbots, Facebook, Twitter, etc. poses several challenges for downstream NLP tasks. This is attributed to the fact that the intended meaning of a sarcastic text is contrary to what is expressed. Further, the use of code-mix language to express sarcasm is increasing day by day. Current NLP techniques for code-mix data have limited success due to the use of different lexicon, syntax, and scarcity of labeled corpora. To solve the joint problem of code-mixing and sarcasm detection, we propose the idea of capturing incongruity through sub-word level embeddings learned via fastText. Empirical results show that our proposed model achieves an F1-score on code-mix Hinglish dataset comparable to pretrained multilingual models while training 10x faster and using a lower memory footprint.