39 citations · 40 across the 5 of their papers we have counts for
7 papers
Entity Image and Mixed-Modal Image Retrieval Datasets
Cristian-Ioan Blaga, Paul Suganthan, Sahil Dua +6
Despite advances in multimodal learning, challenging benchmarks for mixed-modal image retrieval that combines visual and textual information are lacking. This paper introduces a no…
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks
Paul Suganthan, Fedor Moiseev, Le Yan +7
Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language proc…
ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task
Mohammed Khalilia, Sanad Malaysha, Reem Suwaileh +4
This paper presents an overview of the Arabic Natural Language Understanding (ArabicNLU 2024) shared task, focusing on two subtasks: Word Sense Disambiguation (WSD) and Location Me…
The FIGNEWS Shared Task on News Media Narratives
Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash +5
We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses bias and propaganda annota…
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives
Fedor Moiseev, Gustavo Hernandez Abrego, Peter Dornbach +3
Dual encoders have been used for retrieval tasks and representation learning with good results. A standard way to train dual encoders is using a contrastive loss with in-batch nega…
Exploring Dual Encoder Architectures for Question Answering
Zhe Dong, Jianmo Ni, Daniel M. Bikel +4
Dual encoders have been used for question-answering (QA) and information retrieval (IR) tasks with good results. Previous research focuses on two major types of dual encoders, Siam…
Off-policy evaluation for slate recommendation
Adith Swaminathan, Akshay Krishnamurthy, Alekh Agarwal +4
This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendat…