Search Results for author:
Found 14 papers, 3 papers with code
ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021
Social media platforms such as Twitter and Facebook have been utilised for various research studies, from the cohort-level discussion to community-driven approaches to address the challenges in utilizing social media data for health, clinical and biomedical information.
Cross-lingual Sentence Embedding using Multi-Task Learning
We demonstrate the efficacy of an unsupervised as well as a weakly supervised variant of our framework on STS, BUCC and Tatoeba benchmark tasks.
"Confidently Nonsensical?'': A Critical Survey on the Perspectives and Challenges of 'Hallucinations' in NLP
We investigate how hallucination in large language models (LLM) is characterized in peer-reviewed literature using a critical examination of 103 publications across NLP research.
Social Media Ready Caption Generation for Brands
Our architecture is based on two parts: a the first part contains an image captioning model that takes in an image that the brand wants to post online and gives a plain English caption; b the second part takes in the generated caption along with the target brand personality and outputs a catchy personality-aligned social media caption.
Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering
We address the task of evidence retrieval for long document question answering, which involves locating relevant paragraphs within a document to answer a question.
Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages
We train an encoder to gain morphological knowledge of a language and transfer the knowledge to perform unsupervised and weakly-supervised cognate detection tasks with and without the pivot language for the closely-related languages.
Iterative Multi-granular Image Editing using Diffusion Models
We hope our work would attract attention to this newly identified, pragmatic problem setting.
CoPL: Contextual Prompt Learning for Vision-Language Understanding
Our key innovations over earlier works include using local image features as part of the prompt learning process, and more crucially, learning to weight these prompts based on local features that are appropriate for the task at hand.
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
First, our attention segregation loss reduces the cross-attention overlap between attention maps of different concepts in the text prompt, thereby reducing the confusion/conflict among various concepts and the eventual capture of all concepts in the generated output.
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
Prompting pre-trained language models leads to promising results across natural language processing tasks but is less effective when applied in low-resource domains, due to the domain gap between the pre-training data and the downstream task.
Unsupervised Deep Language and Dialect Identification for Short Texts
Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines.
Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation
Conversational recommender systems focus on the task of suggesting products to users based on the conversation flow.
ULD@NUIG at SemEval-2020 Task 9: Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text
Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons.
A Comparative Study of Different State-of-the-Art Hate Speech Detection Methods in Hindi-English Code-Mixed Data
Hate speech detection in social media communication has become one of the primary concerns to avoid conflicts and curb undesired activities.
