Semantics-Aware Inferential Network for Natural Language Understanding
This work addresses the need for better modeling in natural language understanding tasks, offering a novel approach that enhances performance on specific benchmarks.
The authors tackled the problem of improving natural language understanding by proposing a Semantics-Aware Inferential Network (SAIN) that integrates explicit semantics and iterative reasoning, achieving significant improvements on 11 tasks including machine reading comprehension and natural language inference.
For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism. By stringing these steps, the inferential network effectively learns to perform iterative reasoning which incorporates both explicit semantics and contextualized representations. In terms of well pre-trained language models as front-end encoder, our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference.