Commonsense Causal Reasoning
6 papers with code • 0 benchmarks • 1 datasets
"Commonsense Causal Reasoning is the process of capturing and understanding the causal dependencies amongst events and actions." Luo, Zhiyi, et al. "Commonsense causal reasoning between short texts." Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning. 2016.
Benchmarks
These leaderboards are used to track progress in Commonsense Causal Reasoning
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Most implemented papers
Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models
badbadcode/weakCOPA
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ACL 2021
We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model's generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect.
HeadlineCause: A Dataset of News Headlines for Detecting Causalities
ilyagusev/headlinecause
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LREC 2022
In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines.
Knowledge-Augmented Language Models for Cause-Effect Relation Classification
Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models.
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective
This paper proposes a new task to detect commonsense causation between two events in an event sequence (i. e., context), called contextualized commonsense causal reasoning.
CLadder: Assessing Causal Reasoning in Language Models
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
