7 citations · 20 across the 10 of their papers we have counts for
10 papers
Reason2Decide: Rationale-Driven Multi-Task Learning
H M Quamran Hasan, Housam Khalifa Bashier, Jiayi Dai +2
Despite the wide adoption of Large Language Models (LLM)s, clinical decision support systems face a critical challenge: achieving high predictive accuracy while generating explanat…
Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
Ha-Thanh Nguyen, Randy Goebel, Francesca Toni +2
The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal text…
Explaining Autonomous Driving Actions with Visual Question Answering
Shahin Atakishiyev, Mohammad Salameh, Housam Babiker +1
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algor…
A negation detection assessment of GPTs: analysis with the xNot360 dataset
Ha Thanh Nguyen, Randy Goebel, Francesca Toni +2
Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. Our study assesses the negation detection performance of Generativ…
How well do SOTA legal reasoning models support abductive reasoning?
Ha-Thanh Nguyen, Randy Goebel, Francesca Toni +2
We examine how well the state-of-the-art (SOTA) models used in legal reasoning support abductive reasoning tasks. Abductive reasoning is a form of logical inference in which a hypo…
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
Deep Temporal Modelling of Clinical Depression through Social Media Text
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1
We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD)…
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1
A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD…
A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language
Nawshad Farruque, Randy Goebel, Osmar Zaiane
We analyze the process of creating word embedding feature representations designed for a learning task when annotated data is scarce, for example, in depressive language detection…
STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text
Nawshad Farruque, Randy Goebel, Osmar Zaiane +1
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data s…