WarwickNLP at SemEval-2024 Task 1: Low-Rank Cross-Encoders for Efficient Semantic Textual Relatedness
Year: 2024
Type: conference-paper
Abstract: This work participates in SemEval 2024 Task 1 on Semantic Textural Relatedness (STR) in Track A (supervised regression) in two languages, English and Moroccan Arabic.The task consists of providing a score of how two sentences relate to each other.The system developed in this work leveraged a cross-encoder with a merged fine-tuned Low-Rank Adapter (LoRA).The system was ranked eighth in English with a Spearman coefficient of 0.842, while Moroccan Arabic was ranked seventh with a score of 0.816.Moreover, various experiments were conducted to see the impact of different models and adapters on the performance and accuracy of the system.
Authors: Fahad Ebrahim, Mike Joy
Institution: University of Warwick
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4401042638