This paper describes the system we developed for SemEval-2024 Task 1,
"Semantic Textual Relatedness for African and Asian Languages." The aim of the
task is to build a model that can identify semantic textual relatedness (STR)
between two sentences of a target language belonging to a collection of African
and Asian languages. We participated in Subtasks A and C and explored
supervised and cross-lingual training leveraging large language models (LLMs).
Pre-trained large language models have been extensively used for machine
translation and semantic similarity. Using a combination of machine translation
and sentence embedding LLMs, we developed a unified STR model, TranSem, for
subtask A and fine-tuned the T5 family of models on the STR data, FineSem, for
use in subtask C. Our model results for 7 languages in subtask A were better
than the official baseline for 3 languages and on par with the baseline for the
remaining 4 languages. Our model results for the 12 languages in subtask C
resulted in 1st place for Africaans, 2nd place for Indonesian, and 3rd place
for English with low performance for the remaining 9 languages.

使用大型语言模型，开发了针对非洲和亚洲语言的语义文本相关性模型 TranSem，并在 SemEval-2024 任务 1 中取得了不错的成绩。