Kasturi Bhattacharjee


2021

pdf bib
Domain and Task-Informed Sample Selection for Cross-Domain Target-based Sentiment Analysis
Kasturi Bhattacharjee | Rashmi Gangadharaiah | Smaranda Muresan
Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021)

pdf bib
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Elsbeth Turcan | Shuai Wang | Rishita Anubhai | Kasturi Bhattacharjee | Yaser Al-Onaizan | Smaranda Muresan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

pdf bib
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Kasturi Bhattacharjee | Miguel Ballesteros | Rishita Anubhai | Smaranda Muresan | Jie Ma | Faisal Ladhak | Yaser Al-Onaizan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success. However, training these models can be costly both from an economic and environmental standpoint. In this work, we investigate how to effectively use unlabeled data: by exploring the task-specific semi-supervised approach, Cross-View Training (CVT) and comparing it with task-agnostic BERT in multiple settings that include domain and task relevant English data. CVT uses a much lighter model architecture and we show that it achieves similar performance to BERT on a set of sequence tagging tasks, with lesser financial and environmental impact.

2019

pdf bib
Neural Word Decomposition Models for Abusive Language Detection
Sravan Bodapati | Spandana Gella | Kasturi Bhattacharjee | Yaser Al-Onaizan
Proceedings of the Third Workshop on Abusive Language Online

The text we see in social media suffers from lots of undesired characterstics like hatespeech, abusive language, insults etc. The nature of this text is also very different compared to the traditional text we see in news with lots of obfuscated words, intended typos. This poses several robustness challenges to many natural language processing (NLP) techniques developed for traditional text. Many techniques proposed in the recent times such as charecter encoding models, subword models, byte pair encoding to extract subwords can aid in dealing with few of these nuances. In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others. We experiment with recent advances of finetuning pretrained language models, and demonstrate their robustness to domain shift. We also show our approaches achieve state of the art performance on Wikipedia attack, toxicity datasets, and Twitter hatespeech dataset.