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Found 28 papers, 5 papers with code
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis
Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding.
Kawarith: an Arabic Twitter Corpus for Crisis Events
Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research.
UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation
This paper describes the participation of the UoB-NLP team in the ProfNER-ST shared subtask 7a.
GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
In this paper, we develop a Graph-Based Unsupervised Summarization(GUSUM) method for extractive text summarization based on the principle of including the most important sentences while excluding sentences with similar meanings in the summary.
Classifying Arabic Crisis Tweets using Data Selection and Pre-trained Language Models
User-generated Social Media (SM) content has been explored as a valuable and accessible source of data about crises to enhance situational awareness and support humanitarian response efforts.
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #18 on
Visual Question Answering
on MM-Vet
Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text
Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages.
The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery
In this work, we propose a novel deep learning framework: a self-supervised spectral-spatial attention-based vision transformer (SSVT).
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.
This paper describes our submission to SemEval 2021 Task 2.
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability
To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children's performance on tests of mindreading, consisting of 10, 320 question-answer pairs.
``What is on your mind?'' Automated Scoring of Mindreading in Childhood and Early Adolescence
In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence.
BhamNLP at SemEval-2020 Task 12: An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media
Social media platforms such as Twitter offer people an opportunity to publish short posts in which they can share their opinions and perspectives.
"What is on your mind?" Automated Scoring of Mindreading in Childhood and Early Adolescence
In this paper we present the first work on the automated scoring of mindreading ability in middle childhood and early adolescence.
Augmenting Neural Metaphor Detection with Concreteness
The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while.
Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic
A key challenge was the uniqueness of the language used on social media, prompting the out-of-vocabulary (OOV) problem.
On Physical Adversarial Patches for Object Detection
In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector.
Integrating Question Classification and Deep Learning for improved Answer Selection
We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection.
High Accuracy Rule-based Question Classification using Question Syntax and Semantics
We present in this paper a purely rule-based system for Question Classification which we divide into two parts: The first is the extraction of relevant words from a question by use of its structure, and the second is the classification of questions based on rules that associate these words to Concepts.
Ranked #1 on
Text Classification
on TREC-50
Building Text-to-Speech Systems for Resource Poor Languages
This paper describes research on building text-to-speech synthesis systems (TTS) for resource poor languages using available resources from other languages and describes our general approach to building cross-linguistic polyglot TTS.
