Syntax-based Attention Model for Natural Language Inference
This work addresses a specific bottleneck in NLP models by making attention more interpretable and syntax-aware, though it is incremental as it builds on existing attentional mechanisms.
The paper tackled the problem of improving attention mechanisms in neural networks for natural language inference by incorporating syntactic tree structures instead of flat topologies, resulting in enhanced interpretability and efficacy as demonstrated through qualitative analysis.
Introducing attentional mechanism in neural network is a powerful concept, and has achieved impressive results in many natural language processing tasks. However, most of the existing models impose attentional distribution on a flat topology, namely the entire input representation sequence. Clearly, any well-formed sentence has its accompanying syntactic tree structure, which is a much rich topology. Applying attention to such topology not only exploits the underlying syntax, but also makes attention more interpretable. In this paper, we explore this direction in the context of natural language inference. The results demonstrate its efficacy. We also perform extensive qualitative analysis, deriving insights and intuitions of why and how our model works.