论文标题
语义知觉的推论网络，用于自然语言理解
Semantics-Aware Inferential Network for Natural Language Understanding
论文作者
论文摘要
对于自然语言理解任务，无论是机器阅读理解还是自然语言推断，语义意识和推断都是相关建模的有利特征，以更好地理解性能。因此，我们提出了一个语义感知的推论网络（SAIN），以实现这种动机。以明确的上下文化语义为互补输入，Sain的推论模块可以通过注意机制进行一系列关于语义线索的推理步骤。通过串制这些步骤，推论网络有效地学会了进行迭代推理，该推理同时结合了明确的语义和上下文化表示。在作为前端编码器的良好训练的语言模型方面，我们的模型在包括机器阅读理解和自然语言推断在内的11个任务上取得了重大改进。
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.
