Summary of A General and Flexible Multi-concept Parsing Framework For Multilingual Semantic Matching, by Dong Yao
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching
by Dong Yao
First submitted to arxiv on: 5 Mar 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 proposed DC-Match method tackles sentence semantic matching by disentangling keywords from intents, optimizing matching performance. While effective, it relies heavily on external NER techniques, limiting its application to minor languages. To overcome this limitation, the authors develop a Multi-Concept Parsed Semantic Matching (MCP-SM) framework using pre-trained language models. This framework extracts various concepts and infuses them into classification tokens. The approach is demonstrated on English datasets QQP and MRPC, as well as Chinese dataset Medical-SM, and shows promising results on Arabic datasets MQ2Q and XNLI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in natural language processing called sentence semantic matching. It’s like trying to understand what someone means when they say something. Most methods try to understand the meaning of two sentences together, but this new method looks at each sentence separately and tries to figure out what the important words are (called keywords) and what the person wants to do with those words (called intents). This makes it better at matching up sentences that have similar meanings. The authors also tested their method on languages other than English, like Chinese and Arabic, and showed that it works well even when there’s not a lot of data available. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Ner
