# From Discourse to Narrative: Knowledge Projection for Event Relation Extraction

arXiv: https://arxiv.org/abs/2106.08629 · HTML: https://mlsanity.com/paper/2106.08629/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [SpotTune: Transfer Learning through Adaptive Fine-tuning](https://mlsanity.com/paper/1811.08737/) (2018)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018)
- [Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension](https://mlsanity.com/paper/1805.06975/) (2018)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [EventKG: A Multilingual Event-Centric Temporal Knowledge Graph](https://mlsanity.com/paper/1804.04526/) (2018)
- [Variational Neural Machine Translation](https://mlsanity.com/paper/1605.07869/) (2016)
- [A Surprisingly Robust Trick for Winograd Schema Challenge](https://mlsanity.com/paper/1905.06290/) (2019)
- [BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance](https://mlsanity.com/paper/2010.06133/) (2020)
- [Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2004.11861/) (2020)
- [Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/1807.05154/) (2018)
- [A Knowledge Hunting Framework for Common Sense Reasoning](https://mlsanity.com/paper/1810.01375/) (2018)
- [Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations](https://mlsanity.com/paper/2006.11824/) (2020)
- [Variational Neural Discourse Relation Recognizer](https://mlsanity.com/paper/1603.03876/) (2016)

## Built on by (most cited first, 14 of 14)

- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://mlsanity.com/paper/2301.02724/) (2023)
- [A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2112.11740/) (2021)
- [What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification](https://mlsanity.com/paper/2204.13516/) (2022)
- [NarraBench: A Comprehensive Framework for Narrative Benchmarking](https://mlsanity.com/paper/2510.09869/) (2025)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2402.15080/) (2024)
- [Multi-Label Classification for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2406.04461/) (2024)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2211.13873/) (2022)
- [Implicit Discourse Relation Classification For Nigerian Pidgin](https://mlsanity.com/paper/2406.18776/) (2024)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2311.00367/) (2023)
- [Prompting Implicit Discourse Relation Annotation](https://mlsanity.com/paper/2402.04918/) (2024)
- [Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs](https://mlsanity.com/paper/2602.21763/) (2026)
- [SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models](https://mlsanity.com/paper/2603.21720/) (2026)

## Abstract (arXiv)

Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance, and extrinsic experimental results verify the value of the extracted event relations.