# Learning Structured Text Representations

arXiv: https://arxiv.org/abs/1705.09207 · HTML: https://mlsanity.com/paper/1705.09207/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2017.

## Built on by (most cited first, 24 of 24)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Estimating the Personality of White-Box Language Models](https://mlsanity.com/paper/2204.12000/) (2022)
- [Attribute Alignment: Controlling Text Generation from Pre-trained Language Models](https://mlsanity.com/paper/2103.11070/) (2021)
- [Modeling Hierarchical Structures with Continuous Recursive Neural Networks](https://mlsanity.com/paper/2106.06038/) (2021)
- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization](https://mlsanity.com/paper/2106.01317/) (2021)
- [Predicting Discourse Trees from Transformer-based Neural Summarizers](https://mlsanity.com/paper/2104.07058/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference](https://mlsanity.com/paper/2208.04580/) (2022)
- [Advancements in Scientific Controllable Text Generation Methods](https://mlsanity.com/paper/2307.05538/) (2023)
- [To be Closer: Learning to Link up Aspects with Opinions](https://mlsanity.com/paper/2109.08382/) (2021)
- [Improving Topic Segmentation by Injecting Discourse Dependencies](https://mlsanity.com/paper/2209.08626/) (2022)
- [Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues](https://mlsanity.com/paper/2302.05895/) (2023)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [Efficient Beam Tree Recursion](https://mlsanity.com/paper/2307.10779/) (2023)
- [Learning with Latent Structures in Natural Language Processing: A Survey](https://mlsanity.com/paper/2201.00490/) (2022)
- [Discourse-Aware Graph Networks for Textual Logical Reasoning](https://mlsanity.com/paper/2207.01450/) (2022)
- [Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification](https://mlsanity.com/paper/2306.05553/) (2023)
- [To be Continuous, or to be Discrete, Those are Bits of Questions](https://mlsanity.com/paper/2406.07812/) (2024)
- [Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2109.05182/) (2021)
- [An Overview on Controllable Text Generation via Variational Auto-Encoders](https://mlsanity.com/paper/2211.07954/) (2022)
- [Learning Unbiased News Article Representations: A Knowledge-Infused Approach](https://mlsanity.com/paper/2309.05981/) (2023)
- [Testing the Robustness of a BiLSTM-based Structural Story Classifier](https://mlsanity.com/paper/2201.02733/) (2022)

## Abstract (arXiv)

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.