Summary of Sharif-str at Semeval-2024 Task 1: Transformer As a Regression Model For Fine-grained Scoring Of Textual Semantic Relations, by Seyedeh Fatemeh Ebrahimi et al.
Sharif-STR at SemEval-2024 Task 1: Transformer as a Regression Model for Fine-Grained Scoring of Textual Semantic Relations
by Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani, Hadi Alizadeh, Zeinab Sadat Taghavi, Hossein Sameti
First submitted to arxiv on: 17 Jul 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 This paper explores semantic textual relatedness (STR) in natural language processing, with applications across various domains. The authors investigate sentence-level STR using fine-tuning techniques on the RoBERTa transformer, focusing on assessing its efficacy across different languages. Leveraging large language models has led to a paradigm shift in STR methodologies, replacing traditional knowledge-based and statistical approaches. The study demonstrates promising advancements in STR performance, with notable improvements in English (correlation of 0.82, rank 19) and Spanish (correlation of 0.67, rank 15). However, challenges were encountered in languages like Arabic, where the correlation was only 0.38, resulting in a 20th rank. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand the meaning behind words and sentences. It’s called semantic textual relatedness (STR) and it’s important for things like search engines and language translation. Right now, there are different ways to do STR, but this study uses a special kind of computer program called RoBERTa to make it better. The researchers tested their approach on many languages and found that it worked really well in some, like English and Spanish. But it struggled with others, like Arabic. This research can help us create more accurate and helpful language systems. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Transformer » Translation
