Multi-Granularity Reasoning for Natural Language Inference
Quick Answer
This paper shows that The Multi-Granularity Reasoning Network (MGRN) enhances Natural Language Inference by leveraging hierarchical semantic features, outperforming baseline models on public benchmarks.
Quick Take
This approach mimics human cognitive processes, improving reasoning from shallow to deep semantic levels.
Key Points
- MGRN addresses limitations of existing transformer-based models in NLI tasks.
- The framework integrates semantic information across multiple granularities.
- Extensive experiments show MGRN consistently outperforms strong baseline models.
- MGRN mimics human cognitive processes for improved language understanding.
- The approach captures complex semantic interactions effectively.
Paper Resources
Source Excerpt
arXiv:2606. 05181v1 Announce Type: new Abstract: Natural Language Inference (NLI) is a fundamental task in natural language understanding that requires determining the logical relationship between a premise and a hypothesis. Despite the remarkable success of transformer-based pre-trained models, most existing approaches primarily rely on the final-layer token representations, which are often insufficient for capturing the complex and hierarchical semantic interactions required for effective reasoning.
In particular, fine-grained lexical cues, phrasal compositions, and higher-level contextual semantics are typically entangled or diluted in a single representation space. …
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