Semantics-Aware Inferential Network for Natural Language Understanding

TL;DR

This paper introduces SAIN, a semantics-aware inferential network that enhances natural language understanding by performing iterative reasoning over explicit semantics and contextualized representations, leading to improved performance across multiple tasks.

Contribution

The paper proposes a novel SAIN model that integrates explicit semantics with iterative reasoning, improving performance on various NLP understanding tasks.

Findings

Achieves significant improvement on 11 NLP tasks.

Effectively combines explicit semantics with contextualized representations.

Enables iterative reasoning over semantic clues.

Abstract

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.