# Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs

arXiv: https://arxiv.org/abs/2210.11787 · HTML: https://mlsanity.com/paper/2210.11787/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events](https://mlsanity.com/paper/2004.04295/) (2020)
- [A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events](https://mlsanity.com/paper/1707.07343/) (2017)
- [Exploring Contextualized Neural Language Models for Temporal Dependency Parsing](https://mlsanity.com/paper/2004.14577/) (2020)
- [Temporal Event Knowledge Acquisition via Identifying Narratives](https://mlsanity.com/paper/1805.10956/) (2018)
- [Neural Ranking Models for Temporal Dependency Structure Parsing](https://mlsanity.com/paper/1809.00370/) (2018)
- [Structured Interpretation of Temporal Relations](https://mlsanity.com/paper/1808.07599/) (2018)
- [A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously](https://mlsanity.com/paper/1707.09410/) (2017)
- [Using Context Events in Neural Network Models for Event Temporal Status Identification](https://mlsanity.com/paper/1710.04344/) (2017)

## Built on by (most cited first, 2 of 2)

- [Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering](https://mlsanity.com/paper/2310.19292/) (2023)
- [Semi-supervised News Discourse Profiling with Contrastive Learning](https://mlsanity.com/paper/2309.11692/) (2023)

## Abstract (arXiv)

We propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs. Our key observation is that the functional roles of sentences used for profiling news discourse signify different time frames relevant to a news story and can, therefore, help to recover the global temporal structure of a document. Our analyses and experiments with the widely used knowledge distillation technique show that discourse profiling effectively identifies distant inter-sentence event and (or) time expression pairs that are temporally related and otherwise difficult to locate.