Interesting Culture: Social Relation Recognition from Videos via Culture De-confounding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a culturally-specific cultural context can be used to train relationship recognition models . cultural confounding factors can be learned, limiting ability to recognize social relationships in different cultures. |
| Approach: | They propose a culturally-based model that mitigates the influence of culture . they also construct a video social relation recognition dataset to facilitate discussion . |
| Outcome: | The proposed model surpasses state-of-the-art methods on several datasets. |
Similar Papers
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Current knowledge is limited on whether cultural features can predict cross-cultural transfer learning success for subjective tasks. |
| Approach: | They advocate integration of cultural information into datasets and cultural adaptability . findings suggest cultural features can predict cross-cultural transfer learning success . |
| Outcome: | The findings suggest that cultural features can predict cross-cultural transfer learning success in OLD tasks. |
Know Who Your Friends Are: Understanding Social Connections from Unstructured Text (N18-5)
Copied to clipboard
Léa Deleris, Francesca Bonin, Elizabeth Daly, Stéphane Deparis, Yufang Hou, Charles Jochim, Yassine Lassoued, Killian Levacher
| Challenge: | Having an understanding of interpersonal relationships is helpful in many contexts. |
| Approach: | They propose a system that extracts qualitative and quantitative information from texts and aggregates it to provide a condensed view of relationships. |
| Outcome: | The proposed system extracts qualitative and quantitative information elements about interactions and aggregates those to provide a condensed view of relationships. |
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)
Copied to clipboard
Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi
| Challenge: | Existing studies rely on entity information for sentence-level relation extraction (RE) but this can leak superficial and spurious clues of relations. |
| Approach: | They propose to use entity mentions to extract relations from textual context . they use a causal graph to model dependencies between variables in RE models . |
| Outcome: | The proposed method yields significant gains on both effectiveness and generalization for RE. |
From What to Why: Improving Relation Extraction with Rationale Graph (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing neural relation extraction models are limited by entity type and textual context. |
| Approach: | They propose a novel RAtionale Graph to organize co-occurrence constraints among entity types, triggers and relations in a holistic graph view. |
| Outcome: | The proposed method outperforms baselines significantly and achieves state-of-the-art performance on document-level and sentence-level RE benchmarks. |
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
Copied to clipboard
Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
| Approach: | They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE . |
| Outcome: | The proposed methods can extract relational facts from text, but they are still lacking in the current field. |
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
| Approach: | They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples. |
| Outcome: | The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction. |
Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent work in NLP has examined large language models for their understanding of cultural norms across countries, ignoring group consensus or possible multicultural environments. |
| Approach: | They apply cultural consensus theory to the World Values Survey to model multidimensional nuance by ignoring group consensus or over-regularizing consensus. |
| Outcome: | The proposed model misrepresents cultural structures by failing to form cohesive consensus or severely over-regularizing consensus. |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
| Approach: | They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them. |
| Outcome: | The proposed model outperforms the baselines on five public datasets. |
A New Surprise Measure for Extracting Interesting Relationships between Persons (2021.eacl-demos)
Copied to clipboard
| Challenge: | Interesting facts are useful information for a variety of important tasks. |
| Approach: | They propose a method that extracts all personal relationships from dependency trees and calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. |
| Outcome: | The proposed method extracts all personal relationships from dependency trees for the texts and calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. |
Mining Cross-Cultural Differences and Similarities in Social Media (P18-1)
Copied to clipboard
| Challenge: | a new paper examines the problem of computing cross-cultural differences and similarities in natural language understanding . cross-culture differences are important for cross-lingual research, especially in social media . |
| Approach: | They propose a framework for computing cross-cultural differences and similarities from social media . they propose to use a social media platform to find similar terms for slang across languages . |
| Outcome: | The proposed framework outperforms baseline methods on two novel tasks. |