47 citations · 99 across the 12 of their papers we have counts for
13 papers
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Ishani Mondal, Michelle Yuan, Anandhavelu N +5
Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; howe…
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages
Ankan Mullick, Ishani Mondal, Sourjyadip Ray +3
Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for hea…
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues
Shivani Kumar, Ishani Mondal, Md Shad Akhtar +1
Conversations emerge as the primary media for exchanging ideas and conceptions. From the listener's perspective, identifying various affective qualities, such as sarcasm, humour, a…
Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic?
Ishani Mondal, Kabir Ahuja, Mohit Jain +3
The COVID-19 pandemic has brought out both the best and worst of language technology (LT). On one hand, conversational agents for information dissemination and basic diagnosis have…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi +37
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benc…
Multi-Objective Few-shot Learning for Fair Classification
Ishani Mondal, Procheta Sen, Debasis Ganguly
In this paper, we propose a general framework for mitigating the disparities of the predicted classes with respect to secondary attributes within the data (e.g., race, gender etc.)…
End-to-End NLP Knowledge Graph Construction
Ishani Mondal, Yufang Hou, Charles Jochim
This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and…
BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classification
Ishani Mondal
Healthcare predictive analytics aids medical decision-making, diagnosis prediction and drug review analysis. Therefore, prediction accuracy is an important criteria which also nece…
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information
Ishani Mondal
Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medica…
Medical Entity Linking using Triplet Network
Ishani Mondal, Sukannya Purkayastha, Sudeshna Sarkar +4
Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This ta…
Towards Incorporating Entity-specific Knowledge Graph Information in Predicting Drug-Drug Interactions
Ishani Mondal
Off-the-shelf biomedical embeddings obtained from the recently released various pre-trained language models (such as BERT, XLNET) have demonstrated state-of-the-art results (in ter…
ALEX: Active Learning based Enhancement of a Model's Explainability
Ishani Mondal, Debasis Ganguly
An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner. While standard AL heuristics, such…