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A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching

Core Concepts
The author proposes a Multi-Concept Parsed Semantic Matching framework, MCP-SM, to enhance semantic matching across multiple languages by extracting various concepts from sentences. By disentangling keywords and intents without relying on external tools like NER, the MCP-SM offers flexibility and generality in semantic matching tasks.
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.
Sentence semantic matching is a research hotspot in natural language processing. DC-Match disentangles keywords from intents for optimizing matching performance. MCP-SM liberates models from reliance on NER techniques for identifying keywords. Comprehensive experiments conducted on English datasets QQP and MRPC. Experimentation also performed on Chinese dataset Medical-SM. Outstanding performance further proves applicability in low-resource languages like Arabic.
"DC-Match divides sentence semantic matching into two subtasks: keywords matching and intents matching." "Our method eliminates reliance on extra techniques for flexible multilingual semantic matching tasks." "MCP-SM captures interaction between paired sentences through concept injection."

Deeper Inquiries

How does disentangling keywords and intents improve overall model performance?

Disentangling keywords and intents in the context of semantic matching can significantly enhance the model's performance by providing a more nuanced understanding of the input sentences. By separating out keywords, which are essential for capturing the core meaning or topic of a sentence, from intents, which represent the underlying purpose or goal behind a statement, the model can better grasp the subtle nuances in language. This separation allows for a more focused analysis of key elements within sentences, leading to improved semantic matching accuracy. When disentangled, keywords serve as crucial anchors that help identify important terms or concepts within sentences. These keywords act as signposts guiding the model towards relevant information while filtering out noise or irrelevant details. On the other hand, intents provide insights into why certain words are used in specific contexts and help discern underlying motivations or objectives behind statements. By considering both aspects separately and then integrating them back into classification tokens through methods like MCP-SM's Concept Injector module, models can leverage this enriched semantic information to make more accurate predictions about sentence similarity.

How can this approach be adapted for other NLP tasks beyond semantic matching?

The approach of disentangling keywords and intents to enhance model performance is not limited to semantic matching tasks but can also be applied across various Natural Language Processing (NLP) domains. Here are some ways this framework could be adapted for other NLP tasks: Text Summarization: In text summarization tasks, identifying key phrases (keywords) and understanding underlying intentions (intents) behind content can aid in generating concise summaries that capture essential information effectively. Sentiment Analysis: Dissecting sentiments expressed in text by isolating sentiment-bearing words (keywords) from contextual cues indicating emotions (intents) could lead to more precise sentiment classification models. Named Entity Recognition: Separating entities (keywords) from their roles or relationships within sentences (intents) could improve Named Entity Recognition systems' accuracy by focusing on entity identification independent of context. Question Answering Systems: Identifying question-specific terms (keywords) along with intended actions or queries embedded in questions (intents) may facilitate better responses generation by understanding user needs comprehensively. By adapting this disentanglement framework to these diverse NLP applications and tailoring it according to specific task requirements, models can gain deeper insights into textual data structures leading to enhanced performance across multiple linguistic contexts.

What are potential challenges faced when extending this framework to even more diverse linguistic contexts?

Extending frameworks like MCP-SM to diverse linguistic contexts comes with several challenges that need careful consideration: Language Complexity: Different languages exhibit unique syntactic structures, word order variations, idiomatic expressions making it challenging to generalize keyword-intent dissection techniques across all languages accurately. Data Availability: Obtaining labeled datasets for training models across numerous languages might be difficult due to resource constraints resulting in biased models favoring well-represented languages over others. Cross-Linguistic Variability: Languages vary significantly concerning grammar rules, vocabulary richness posing difficulties in designing universal parsing strategies adaptable across all linguistic backgrounds. 4..NER Performance Variation: The effectiveness of Named Entity Recognition tools varies among languages; hence reliance on external tools may impact system robustness especially when dealing with low-resource languages lacking sophisticated NER resources. To address these challenges successfully during extension efforts requires thorough research on multilingual NLP methodologies ensuring adaptability while accounting for linguistic diversity intricacies present globally .