Learning Structured Text Representations

Code

• github.com/nlpyang/structured
  OfficialIn papertf★ 0
• github.com/JepsonWong/Text_Classification
  tf★ 51
• github.com/elisaF/structured
  tf★ 0

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.

Reproductions