%0 Generic %1 liu2017learning %A Liu, Yang %A Lapata, Mirella %D 2017 %K modelling naacl2018 neuralnet rnn session2 structure %T Learning Structured Text Representations %U http://arxiv.org/abs/1705.09207 %X In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.

@misc{liu2017learning, abstract = {In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.}, added-at = {2018-06-02T19:18:37.000+0200}, author = {Liu, Yang and Lapata, Mirella}, biburl = {https://www.bibsonomy.org/bibtex/2cc1d432509fefe4897db4d0fdd659875/albinzehe}, description = {[1705.09207] Learning Structured Text Representations}, interhash = {cd39f66c87adbc7deaa66fa8bd457ff3}, intrahash = {cc1d432509fefe4897db4d0fdd659875}, keywords = {modelling naacl2018 neuralnet rnn session2 structure}, note = {cite arxiv:1705.09207Comment: change to one-based indexing, published in Transactions of the Association for Computational Linguistics (TACL), https://transacl.org/ojs/index.php/tacl/article/view/1185/280}, timestamp = {2018-06-02T19:18:37.000+0200}, title = {Learning Structured Text Representations}, url = {http://arxiv.org/abs/1705.09207}, year = 2017 }