The content introduces the MCP-SM framework for multilingual semantic matching, emphasizing the importance of disentangling keywords and intents. The approach aims to liberate models from dependency on NER tools, enhancing performance across different languages. Experimental results demonstrate the effectiveness of MCP-SM in improving semantic matching accuracy.
The paper discusses the significance of sentence semantic matching in various applications such as search engines, chatbots, and recommendation systems. It highlights the limitations of existing models that neglect keywords and intents concepts in sentence semantics.
To address these limitations, the authors propose the MCP-SM framework based on pre-trained language models to extract multiple concepts from sentences. This approach aims to enhance classification tokens with additional semantic information for accurate matching.
Experimental evaluations on English datasets QQP and MRPC, Chinese dataset Medical-SM, and Arabic datasets MQ2Q and XNLI showcase the superior performance of MCP-SM compared to DC-Match. The results indicate that parsing sentences into multiple concepts improves semantic matching accuracy across different languages.
Overall, the content presents a novel approach to enhance multilingual semantic matching by extracting various concepts from sentences without relying on external tools like NER techniques. The experimental results validate the effectiveness of the proposed MCP-SM framework in improving semantic matching accuracy across diverse languages.
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arxiv.org
Key Insights Distilled From
by Dong Yao,Asa... at arxiv.org 03-06-2024
https://arxiv.org/pdf/2403.02975.pdfDeeper Inquiries