Papers by Kasturi Bhattacharjee
What Do Users Care About? Detecting Actionable Insights from User Feedback (2022.naacl-industry)
Copied to clipboard
| Challenge: | a large amount of data can be used to extract actionable insights from user feedback . however, the data is unstructured and voluminous, and is underutilized for most users . |
| Approach: | They propose an unsupervised method for finding actionable insights from user feedback . they cluster data into groups containing coherent insights, followed by theme detection . |
| Outcome: | The proposed approach outperforms baselines on two real-world user feedback datasets and one academic dataset. |
PerKGQA: Question Answering over Personalized Knowledge Graphs (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for question answering over knowledge graphs have focused on generalizable or generic knowledge, which assumes there is a predefined global KG for all queries. |
| Approach: | They propose to use a non-parametric technique that employs case-based reasoning and a parametric approach using graph neural networks to query a predefined knowledge graph (KG) |
| Outcome: | The proposed methods outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%. |
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging (2020.emnlp-main)
Copied to clipboard
Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan
| Challenge: | Using large amounts of unlabeled data to improve performance has become the foundation for many natural language processing tasks. |
| Approach: | They propose a task-specific semi-supervised approach that uses unlabeled data in a more task-agnostic manner. |
| Outcome: | The proposed approach achieves similar performance to BERT on a set of sequence tagging tasks with less financial and environmental impact. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
Copied to clipboard
Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |