Deep Attentive Ranking Networks For Learning To Order Sentences | Awesome LLM Papers Add your paper to Awesome LLM Papers

Deep Attentive Ranking Networks For Learning To Order Sentences

Pawan Kumar, Dhanajit Brahma, Harish Karnick, Piyush Rai . Proceedings of the AAAI Conference on Artificial Intelligence 2020 – 44 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
AAAI Compositional Generalization Evaluation Interdisciplinary Approaches Model Architecture Tools Training Techniques

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.

Similar Work