Search Results for author:
Found 31 papers, 14 papers with code
Distilling Text Style Transfer With Self-Explanation From LLMs
Text Style Transfer (TST) seeks to alter the style of text while retaining its core content.
SPAR: Personalized Content-Based Recommendation via Long Engagement Attention
Leveraging users' long engagement histories is essential for personalized content recommendations.
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks.
NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task
We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023).
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages
We evaluate the performance of various multilingual pretrained language models (e. g., mT5) and instruction-tuned LLMs (e. g., BLOOMZ, ChatGPT) on SPARROW through fine-tuning, zero-shot, and/or few-shot learning.
Detecting the Anomalies in LiDAR Pointcloud
LiDAR sensors play an important role in the perception stack of modern autonomous driving systems.
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
Despite these advances, there remains a significant gap in understanding the impact of different SSCL strategies on time series forecasting performance, as well as the specific benefits that SSCL can bring.
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions
The results demonstrate that our proposed LaMini-LM models are comparable to competitive baselines, while being much smaller in size.
Ranked #15 on
Word Sense Disambiguation
on Words in Context
Edge Enhanced Image Style Transfer via Transformers
In recent years, arbitrary image style transfer has attracted more and more attention.
S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention
Transformer's recent integration into style transfer leverages its proficiency in establishing long-range dependencies, albeit at the expense of attenuated local modeling.
NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task
We describe findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI 2022).
Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling
This study is to explore how to use natural language processing (NLP) methods, especially unsupervised machine learning, to assist psychologists to analyze emotions and sentiments and to use topic modeling to identify common issues and challenges that young people with IDD and their families have.
Automated Utterance Labeling of Conversations Using Natural Language Processing
Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors.
Decay No More: A Persistent Twitter Dataset for Learning Social Meaning
With the proliferation of social media, many studies resort to social media to construct datasets for developing social meaning understanding systems.
Contrastive Learning of Sociopragmatic Meaning in Social Media
Recent progress in representation and contrastive learning in NLP has not widely considered the class of \textit{sociopragmatic meaning} (i. e., meaning in interaction within different language communities).
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning
We test our models on $15$ different Twitter datasets for social meaning detection.
NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
This Shared Task includes four subtasks: country-level Modern Standard Arabic (MSA) identification (Subtask 1. 1), country-level dialect identification (Subtask 1. 2), province-level MSA identification (Subtask 2. 1), and province-level sub-dialect identification (Subtask 2. 2).
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task
The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain.
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments
Although the prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties.
Leveraging Affective Bidirectional Transformers for Offensive Language Detection
Social media are pervasive in our life, making it necessary to ensure safe online experiences by detecting and removing offensive and hate speech.
Wrapper Feature Selection Algorithm for the Optimization of an Indicator System of Patent Value Assessment
Verification experiments on multiple UCI standard datasets indicated that the algorithm effectively reduced the size of the feature set and significantly enhanced the prediction accuracy of the classifier.
AraNet: A Deep Learning Toolkit for Arabic Social Media
We describe AraNet, a collection of deep learning Arabic social media processing tools.
Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
In this work we exploit a newly-created Arabic dataset with ground truth age and gender labels to learn these attributes both individually and in a multi-task setting at the sentence level.
DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect
Prediction of language varieties and dialects is an important language processing task, with a wide range of applications.
BERT-Based Arabic Social Media Author Profiling
We report our models for detecting age, language variety, and gender from social media data in the context of the Arabic author profiling and deception detection shared task (APDA).
Multi-Task Bidirectional Transformer Representations for Irony Detection
Supervised deep learning requires large amounts of training data.
No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects
We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification.
UBC-NLP at SemEval-2019 Task 6:Ensemble Learning of Offensive Content With Enhanced Training Data
We examine learning offensive content on Twitter with limited, imbalanced data.
Happy Together: Learning and Understanding Appraisal From Natural Language
In this paper, we explore various approaches for learning two types of appraisal components from happy language.
UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data
We examine learning offensive content on Twitter with limited, imbalanced data.
UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs
We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection.
