mlsanityData to 7 Oct 2026

Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs

arXiv 2210.11787 ↗First posted 21 Oct 2022cs.CLcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 42 references that are in the map. First posted on arXiv 21 Oct 2022.

Builds on

14 of 42 references.

References · 14

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  7. 2020Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Eventscited by 12
  8. 2017A Sequential Model for Classifying Temporal Relations between Intra-Sentence Eventscited by 11
  9. 2020Exploring Contextualized Neural Language Models for Temporal Dependency Parsingcited by 7
  10. 2018Temporal Event Knowledge Acquisition via Identifying Narrativescited by 5
  11. 2018Neural Ranking Models for Temporal Dependency Structure Parsingcited by 3
  12. 2018Structured Interpretation of Temporal Relationscited by 2
2 more
  1. 2017A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneouslycited by 1
  2. 2017Using Context Events in Neural Network Models for Event Temporal Status Identificationcited by 1

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answeringcited by 3
  2. 2023Semi-supervised News Discourse Profiling with Contrastive Learningcited by 0