Summary of Ustcctsu at Semeval-2024 Task 1: Reducing Anisotropy For Cross-lingual Semantic Textual Relatedness Task, by Jianjian Li et al.
USTCCTSU at SemEval-2024 Task 1: Reducing Anisotropy for Cross-lingual Semantic Textual Relatedness Task
by Jianjian Li, Shengwei Liang, Yong Liao, Hongping Deng, Haiyang Yu
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The medium difficulty summary is as follows: The cross-lingual semantic textual relatedness task is crucial for establishing connections between different languages, which is essential for machine translation, multilingual information retrieval, and cross-lingual text understanding. This paper uses the XLM-R-base model with pre-trained sentence representations based on whitening to reduce anisotropy, alleviating the curse of multilingualism. The approach achieves a 2nd score in Spanish, a 3rd in Indonesian, and top ten results in track C’s competition. A comprehensive analysis is provided to inspire future research improving performance on cross-lingual tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The low difficulty summary is as follows: This paper helps computers understand how different languages are related. It’s important for things like translating websites from one language to another or finding relevant information across multiple languages. The researchers used a special computer model and technique to reduce the difficulties of working with many languages at once. Their approach was very good, getting second place in one test and top ten in others. They also studied their results to help guide future research improving these kinds of tasks. |
Keywords
» Artificial intelligence » Translation
