Search Results for author:
Found 15 papers, 7 papers with code
Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy
Compared with classical open cholecystectomy, laparoscopic cholecystectomy (LC) is associated with significantly shorter recovery period, and hence is the preferred method.
EDM3: Event Detection as Multi-task Text Generation
We show that EDM3 helps to learn transferable knowledge that can be leveraged to perform Event Detection and its subtasks concurrently, mitigating the error propagation inherent in pipelined approaches.
Instruction Tuned Models are Quick Learners
In the MTL setting, an instruction tuned model trained on only 6% of downstream training data achieve SOTA, while using 100% of the training data results in a 3. 69% points improvement (ROUGE-L 74. 68) over the previous SOTA.
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution
To initiate a systematic research in this important area of 'dealing with novelties', we introduce 'NoveltyTask', a multi-stage task to evaluate a system's performance on pipelined novelty 'detection' and 'accommodation' tasks.
InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis
In this paper, we present InstructABSA, Aspect Based Sentiment Analysis (ABSA) using the instruction learning paradigm for the ABSA subtasks: Aspect Term Extraction (ATE), Aspect Term Sentiment Classification (ATSC), and Joint Task modeling.
Ranked #1 on
Aspect Extraction
on SemEval 2014 Task 4 Sub Task 2
Detecting Unintended Social Bias in Toxic Language Datasets
However, very little research has been done to detect unintended social bias from these toxic language datasets.
"John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibility
We show that even state-of-the-art models such as GPT-3, GPT-2, and T5 struggle to answer the feasibility questions correctly.
Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning
This paper presents a hybrid online Partially Observable Markov Decision Process (POMDP) planning system that addresses the problem of autonomous navigation in the presence of multi-modal uncertainty introduced by other agents in the environment.
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis.
Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues
Since AI solutions are data intensive and there exists no domain specific data to address the problem of biases in scripts, we introduce a new dataset of movie scripts that are annotated for identity bias.
Context-NER : Contextual Phrase Generation at Scale
In this paper, we introduce CONTEXT-NER, a task that aims to generate the relevant context for entities in a sentence, where the context is a phrase describing the entity but not necessarily present in the sentence.
Ranked #1 on
ContextNER
on EDGAR10-Q Dataset
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell
A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development phase.
An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models
Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices.
Prediction of Students performance with Artificial Neural Network using Demographic Traits
The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution.
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis
Memes have become an ubiquitous social media entity and the processing and analysis of suchmultimodal data is currently an active area of research.
