USTCCTSU在SemEval-2024任务1中的研究：为跨语言语义文本相关性任务降低各向异性
USTCCTSU at SemEval-2024 Task 1: Reducing Anisotropy for Cross-lingual Semantic Textual Relatedness Task
AI总结：
针对跨语言语义文本相关性任务，该研究选择XLM-R-base为基础模型，采用基于白化的预训练句子表示降低各向异性，并设计数据过滤方法缓解多语言诅咒，在竞赛中取得优异成绩并为相关研究提供启发。
摘要：
跨语言语义文本相关性任务是解决跨语言交流与文本理解挑战的重要研究任务，有助于建立不同语言间的语义联系，对机器翻译、多语言信息检索及跨语言文本理解等下游任务至关重要。基于大量对比实验，我们选择XLM-R-base作为基础模型，使用基于白化的预训练句子表示来降低各向异性。此外，针对给定训练数据，我们设计了精细的数据过滤方法以缓解多语言诅咒。通过该方法，我们在西班牙语中获得第2名，印度尼西亚语中获得第3名，在竞赛C赛道中多项结果进入前十。我们进一步进行了全面分析，为未来提升跨语言任务性能的研究提供启发。
英文摘要：
Cross-lingual semantic textual relatedness task is an important research task that addresses challenges in cross-lingual communication and text understanding. It helps establish semantic connections between different languages, crucial for downstream tasks like machine translation, multilingual information retrieval, and cross-lingual text understanding.Based on extensive comparative experiments, we choose the XLM-R-base as our base model and use pre-trained sentence representations based on whitening to reduce anisotropy.Additionally, for the given training data, we design a delicate data filtering method to alleviate the curse of multilingualism. With our approach, we achieve a 2nd score in Spanish, a 3rd in Indonesian, and multiple entries in the top ten results in the competition's track C. We further do a comprehensive analysis to inspire future research aimed at improving performance on cross-lingual tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
