Extracting appropriate features to represent a corpus is an important task for textual mining. Previous attention based work usually enhance feature at the lexical level, which lacks the exploration of feature augmentation at the sentence level. In this paper, we exploit a Dynamic Feature Generation Network (DFGN) to solve this problem. Specifically, DFGN generates features based on a variety of attention mechanisms and attaches features to sentence representation. Then a thresholder is designed to filter the mined features automatically. DFGN extracts the most significant characteristics from datasets to keep its practicability and robustness. Experimental results on multiple well-known answer selection datasets show that our proposed approach significantly outperforms state-of-the-art baselines. We give a detailed analysis of the experiments to illustrate why DFGN provides excellent retrieval and interpretative ability.
翻译:先前的注重工作通常会加强词汇层面的特征,在句级上没有特征增强的探索。在本文中,我们利用动态地貌生成网络(DFGN)解决这个问题。具体地说,DFGN产生基于各种关注机制的特征,并附加句号描述特征。然后设计了一个临界器来自动过滤埋设的特征。DFGN从数据集中提取最重要的特征,以保持其实用性和稳健性。关于多个已知的答案选择数据集的实验结果显示,我们拟议的方法大大超出了最新基线。我们对实验进行了详细分析,以说明为什么DFGN提供了极好的检索和解释能力。