In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-ofthe-art methods.
To address the issue, ATTOrderNet REF employs self-attention at the encoder to capture global dependencies regardless of an input sentence order.
Deep Attentive Sentence Ordering Network
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Computer Science