Search Results for author:
Found 16 papers, 3 papers with code
UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations
To tackle this task, this work proposes to fine-tune different BERT-based models pre-trained on different languages.
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection
Sarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature.
Does Knowledge Graph Really Matter for Recommender Systems?
We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users.
nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States
Inspired by the most recent advances in Chain-of-Thought, in this work, we exploit the existing three-hop reasoning approach (THOR) to perform large language model instruction-tuning for answering: emotion states (THOR-state), and emotion caused by one speaker to the other (THOR-cause).
Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection
The task comprises three subtasks: binary classification in monolingual and multilingual (Subtask A), multi-class classification (Subtask B), and mixed text detection (Subtask C).
Enhancing dysarthria speech feature representation with empirical mode decomposition and Walsh-Hadamard transform
This algorithm effectively addresses the challenges of the imbalanced dataset and non-linearity in dysarthric speech and simultaneously provides a robust representation of the local pathological features of the vocal folds and tracts.
Design, construction and evaluation of emotional multimodal pathological speech database
The automatic recognition tested on speech and glottal data, with average accuracy of 78% for controls and 60% for patients in audio, while 51% for controls and 38% for patients in glottal data, indicating an influence of the disease on emotional expression.
UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning
It shows that the pre-trained BERT token embeddings can be used as additional knowledge for understanding abstract meanings in question answering.
UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection
Humour detection is an interesting but difficult task in NLP.
UOR at SemEval-2021 Task 12: On Crowd Annotations; Learning with Disagreements to optimise crowd truth
In this work, two approaches dealing with the noise and errors in crowd-sourced labels are proposed.
UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis
In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task.
UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation
SemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense.
Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds
In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system.
