@inproceedings{piao-etal-2024-hw,
title = "{HW}-{TSC} 2024 Submission for the {S}em{E}val-2024 Task 1: Semantic Textual Relatedness ({STR})",
author = "Piao, Mengyao and
Chang, Su and
Li, Yuang and
Qiao, Xiaosong and
Zhao, Xiaofeng and
Li, Yinglu and
Zhang, Min and
Yang, Hao",
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-1.232",
doi = "10.18653/v1/2024.semeval-1.232",
pages = "1634--1638",
abstract = "The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. In this paper, we present the system of Huawei Translation Services Center (HW-TSC) for Task 1 of SemEval 2024, which aims to automatically measure the semantic relatedness of sentence pairs in African and Asian languages. The task dataset for this task covers about 14 different languages, These languages originate from five distinct language families and are predominantly spoken in Africa and Asia. For this shared task, we describe our proposed solutions, including ideas and the implementation steps of the task, as well as the outcomes of each experiment on the development dataset. To enhance the performance, we leverage these experimental outcomes and construct an ensemble one. Our results demonstrate that our system achieves impressive performance on test datasets in unsupervised track B and ranked first place for the Punjabi language pair.",
}
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<abstract>The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. In this paper, we present the system of Huawei Translation Services Center (HW-TSC) for Task 1 of SemEval 2024, which aims to automatically measure the semantic relatedness of sentence pairs in African and Asian languages. The task dataset for this task covers about 14 different languages, These languages originate from five distinct language families and are predominantly spoken in Africa and Asia. For this shared task, we describe our proposed solutions, including ideas and the implementation steps of the task, as well as the outcomes of each experiment on the development dataset. To enhance the performance, we leverage these experimental outcomes and construct an ensemble one. Our results demonstrate that our system achieves impressive performance on test datasets in unsupervised track B and ranked first place for the Punjabi language pair.</abstract>
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%0 Conference Proceedings
%T HW-TSC 2024 Submission for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR)
%A Piao, Mengyao
%A Chang, Su
%A Li, Yuang
%A Qiao, Xiaosong
%A Zhao, Xiaofeng
%A Li, Yinglu
%A Zhang, Min
%A Yang, Hao
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F piao-etal-2024-hw
%X The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. In this paper, we present the system of Huawei Translation Services Center (HW-TSC) for Task 1 of SemEval 2024, which aims to automatically measure the semantic relatedness of sentence pairs in African and Asian languages. The task dataset for this task covers about 14 different languages, These languages originate from five distinct language families and are predominantly spoken in Africa and Asia. For this shared task, we describe our proposed solutions, including ideas and the implementation steps of the task, as well as the outcomes of each experiment on the development dataset. To enhance the performance, we leverage these experimental outcomes and construct an ensemble one. Our results demonstrate that our system achieves impressive performance on test datasets in unsupervised track B and ranked first place for the Punjabi language pair.
%R 10.18653/v1/2024.semeval-1.232
%U https://aclanthology.org/2024.semeval-1.232
%U https://doi.org/10.18653/v1/2024.semeval-1.232
%P 1634-1638
Markdown (Informal)
[HW-TSC 2024 Submission for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR)](https://aclanthology.org/2024.semeval-1.232) (Piao et al., SemEval 2024)
ACL
- Mengyao Piao, Su Chang, Yuang Li, Xiaosong Qiao, Xiaofeng Zhao, Yinglu Li, Min Zhang, and Hao Yang. 2024. HW-TSC 2024 Submission for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR). In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1634–1638, Mexico City, Mexico. Association for Computational Linguistics.