Deep Attentive Ranking Networks for Learning to Order Sentences


  • Pawan Kumar IIT Kanpur
  • Dhanajit Brahma IIT Kanpur
  • Harish Karnick IIT Kanpur
  • Piyush Rai IIT Kanpur



We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.




How to Cite

Kumar, P., Brahma, D., Karnick, H., & Rai, P. (2020). Deep Attentive Ranking Networks for Learning to Order Sentences. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8115-8122.



AAAI Technical Track: Natural Language Processing