| Challenge: | Existing methods for reasoning causalities on word level are limited . a word-level causal reasoning method may only predict the unintelligible effect of "quarrel" |
| Approach: | They propose a novel event-level causal reasoning method that structuralizes event-effect event pairs into an event causality network and shows its use in the task of effect generation. |
| Outcome: | The proposed method generates more reasonable effect sentences than well-designed competitors. |
Similar Papers
Causal Graph based Event Reasoning using Semantic Relation Experts (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in event reasoning have limited ability to accurately identify causal connections between events. |
| Approach: | They propose a collaborative approach to generate correct graphs and graphs to assist reasoning . they propose 'a causal chain of events' task that requires a causal link between events . |
| Outcome: | The proposed approach achieves competitive results with state-of-the-art models on forecasting and next event prediction tasks. |
Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
Copied to clipboard
| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
| Outcome: | The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations . |
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models for document-level Event Causality Identification (ECI) are limited to intra-sentence contexts where event mention pairs are presented in the same sentences. |
| Approach: | They propose a deep learning model that accepts inter-sentence event mention pairs . they use interaction graphs to capture relevant connections between important objects . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to empathetic response generation only consider causalities between the user’s emotion and the user's experiences and neglect interdependence among causalities and reason them independently. |
| Approach: | They propose to use a conditional variable Graph Auto-Encoder to reason all plausible causalities interdependently and simultaneously given the user’s emotion, dialogue history, and future dialogue content. |
| Outcome: | The proposed method achieves state-of-the-art in a real-world situation. |
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for identifying causal relations of events are limited . Existing approaches cannot handle well the problem, especially in the condition of lacking training data. |
| Approach: | They propose a Latent Structure Induction Network to integrate external structural knowledge into a causality reasoning task. |
| Outcome: | The proposed approach outperforms existing state-of-the-art methods on two widely used datasets. |
Enhancing Event Causality Identification with Event Causal Label and Event Pair Interaction Graph (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for event causality identification (ECI) do not consider event causal label information and interaction information between event pairs. |
| Approach: | They propose a framework to enrich the representation of event pairs by introducing the event causal label information and the interaction information between event pairs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two benchmark datasets. |
CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
Copied to clipboard
Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
| Approach: | They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods . |
| Outcome: | The proposed methods perform better than existing methods on a range of tasks. |
ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work infers the causation between events based on knowledge from annotated causal event pairs, but additional evidence information is unexploited. |
| Approach: | They propose an Event graph knowledge enhanced explainable CAusal Reasoning framework that acquires additional evidence information from a large-scale causal event graph as logical rules for causal reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in human evaluation and in animal models. |
Causal Inference of Script Knowledge (2020.emnlp-main)
Copied to clipboard
| Challenge: | Prior work on script induction relied on correlation between instances of events in corpus . instead, we propose an approach based on causal effects between events . |
| Approach: | They propose to use causal effects to induce scripts from text . they propose to compute a function that matches the intuition of what a script represents . |
| Outcome: | The proposed method matches the intuition of what a script represents, the authors show . |
CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | Establishing causal relationships is a fundamental goal of scientific research . lack of clear definitions, notations, benchmark datasets, and challenges remains . |
| Approach: | They introduce the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provide an overview of causal perspectives to NLP problems. |
| Outcome: | This tutorial introduces the fundamentals of causal discovery and causal effect estimation to the natural language processing audience and provides an overview of causal perspectives to NLP problems. |