This repository is for the paper UAlberta at SemEval-2024 Task 1: A Potpourri of Methods for Quantifying Multilingual Semantic Textual Relatedness and Similarity. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1798–1805, Mexico City, Mexico. Association for Computational Linguistics.
🏆 1st Place on Track A English among all submitted systems.
- system - Our submitted system
- tutorial - Friendly guidance to start the task easily
- Ning Shi - mrshininnnnn@gmail.com
@inproceedings{shi-etal-2024-ualberta,
title = "{UA}lberta at {S}em{E}val-2024 Task 1: A Potpourri of Methods for Quantifying Multilingual Semantic Textual Relatedness and Similarity",
author = "Shi, Ning and
Li, Senyu and
Luo, Guoqing and
Mirzaei, Amirreza and
Rafiei, Ali and
Riley, Jai and
Sheikhi, Hadi and
Siavashpour, Mahvash and
Tavakoli, Mohammad and
Hauer, Bradley",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.254",
pages = "1798--1805",
abstract = "We describe our systems for SemEval-2024 Task 1: Semantic Textual Relatedness. We investigate the correlation between semantic relatedness and semantic similarity. Specifically, we test two hypotheses: (1) similarity is a special case of relatedness, and (2) semantic relatedness is preserved under translation. We experiment with a variety of approaches which are based on explicit semantics, downstream applications, contextual embeddings, large language models (LLMs), as well as ensembles of methods. We find empirical support for our theoretical insights. In addition, our best ensemble system yields highly competitive results in a number of diverse categories. Our code and data are available on GitHub.",
}