Rui Bai, Di Lu, Shihao Ran, Elizabeth M. Olson, Hemank Lamba, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Existing datasets categorizing conflict events do not cover all of the fine-grained types of conflict relevant to areas like the Horn of Africa. |
| Approach: | They propose to use online news articles to categorize violent conflict events . they propose to extract event-relevance and event-types from 500 English event descriptions . |
| Outcome: | The proposed dataset categorizes conflict risk according to specific areas required by stakeholders in the Humanitarian-Peace-Development Nexus. |
Similar Papers
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics. |
| Approach: | They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations. |
| Outcome: | The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. |
ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence (2022.naacl-main)
Copied to clipboard
Yibo Hu, MohammadSaleh Hosseini, Erick Skorupa Parolin, Javier Osorio, Latifur Khan, Patrick Brandt, Vito D’Orazio
| Challenge: | Traditionally, researchers used manual coding to track conflict processes worldwide, but the high costs and slow pace of domain experts make it difficult and costly to monitor complex and rapidly changing conflicts. |
| Approach: | They propose a domain-specific pre-trained language model for conflict and political violence that can be used to train a language model from scratch and continue training. |
| Outcome: | The proposed model outperforms BERT in conflict research. |
LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World (2025.findings-acl)
Copied to clipboard
Sina Semnani, Pingyue Zhang, Wanyue Zhai, Haozhuo Li, Ryan Beauchamp, Trey Billing, Katayoun Kishi, Manling Li, Monica Lam
| Challenge: | Using a partially reannotated subset of the Armed Conflict Location & Event Data, we analyze 39,786 conflict events across 20 languages and 171 countries. |
| Approach: | They propose a large-scale conflict event dataset with extensive coverage of region-specific entities. |
| Outcome: | The proposed method detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. |
Classifying Dyads for Militarized Conflict Analysis (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing research examines the origins of militarized conflict by examining bi-lateral relationships between entity pairs and multi-lateral relations among multiple entities. |
| Approach: | They propose to use Wikipedia to model dyadic and systemic causes to compare their correlations with conflict between two entities. |
| Outcome: | The proposed graphs show that Wikipedia articles of allies are semantically more similar than enemies. |
Conflicts in Texts: Data, Implications and Challenges (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Conflicts in data could reflect complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. |
| Approach: | This survey categorizes conflicting information into three key areas . they identify the areas where conflicting data can be ignored and undermine models' reliability and trustworthiness. |
| Outcome: | The findings highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively. |
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid (2024.findings-emnlp)
Copied to clipboard
Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth Olson, Henry Dambanemuya, João Bárcia, David Batista, Christina Wille, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Humanitarian organizations can analyze data to discover trends, gather aggregated insights, manage security risks, and inform advocacy and funding proposals. |
| Approach: | They present a dataset comprising news articles in three languages containing instances of different types of violent incidents categorized by the humanitarian sector they impact. |
| Outcome: | The proposed framework can be used to identify violent incidents and identify their impact on humanitarian operations. |
A New Task and Dataset on Detecting Attacks on Human Rights Defenders (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for event extraction cannot extract information from textual sources. |
| Approach: | They propose to use crowdsourced annotations on 500 online news articles to train and evaluate baseline models to predict annotated characteristics. |
| Outcome: | The proposed dataset includes crowdsourced annotations on 500 online news articles and includes fine-grained information about the type and location of attacks, as well as information about victims. |
A Dataset for Multi-lingual Epidemiological Event Extraction (2020.lrec-1)
Copied to clipboard
| Challenge: | Using the Web, we propose a corpus for information extraction and text classification. |
| Approach: | They propose to use a corpus for information extraction and natural language processing (NLP) tasks such as text classification. |
| Outcome: | The proposed corpus can be used for information extraction and natural language processing tasks such as text classification. |
This Land is Your, My Land: Evaluating Geopolitical Bias in Language Models through Territorial Disputes (2024.naacl-long)
Copied to clipboard
| Challenge: | Pretrained large language models may answer differently in different languages . this contrasts with a multilingual human, who would likely answer consistently . |
| Approach: | They propose a dataset of territorial disputes which includes multiple-choice questions in 49 languages . they propose metrics to quantify bias and consistency in responses across different languages based on their data . |
| Outcome: | The proposed model recalls certain knowledge inconsistently when asked in different languages. |
MOKA: Moral Knowledge Augmentation for Moral Event Extraction (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for discerning moral values are limited due to lack of context, lack of moral reasoning capabilities and complexity of moral stances. |
| Approach: | They propose a framework for moral event extraction using moral words and moral scenarios. |
| Outcome: | The proposed framework outperforms baselines across three moral event understanding tasks. |