Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to construct CSKGs with large semantic coverage are expensive and introduce spurious noise. |
| Approach: | They propose a denoising framework that incorporates entity semantic information, global rules, and local structural information from the CSKG. |
| Outcome: | The proposed framework outperforms baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks. |
Similar Papers
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
| Approach: | They propose to use a common framework to solve commonsense reasoning tasks using a dataset from NLI. |
| Outcome: | The proposed method achieves state-of-the-art unsupervised performance on two commonsense reasoning tasks. |
Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an ability to generate unobvious concepts. |
| Approach: | They propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-level relationships between concepts. |
| Outcome: | The proposed framework can capture high-order relationships between concepts in four special cases: long path, path-to-path, router, and graph-node-path. |
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to enhance pre-trained language models (PTMs) with a knowledge-aware graph neural network (GNN) encoder that models a commonsense knowledge graph (CSKG) can't explain how external knowledge resources improve the reasoning capacity of PTMs. |
| Approach: | They propose to use relation features from CSKGs to enhance the reasoning capacity of pre-trained language models (PTMs) by encoding a commonsense knowledge graph (CSKG) |
| Outcome: | The proposed approach reduces the parameters for encoding CSKGs and improves on five benchmarks. |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
Copied to clipboard
| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
| Approach: | They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language. |
| Outcome: | The proposed models can generate rich and diverse commonsense descriptions in natural language. |
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion (2022.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge graph embedding techniques rely on fact-view data to predict missing links between entities, limiting their performance. |
| Approach: | They propose a commonsense-aware knowledge embedding framework which generates commonsensense from factual triples with entity concepts for a KGC task. |
| Outcome: | The proposed framework could produce high-quality negative triples and joint commonsense and fact-view link prediction. |
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning (D19-1)
Copied to clipboard
| Challenge: | empowering machines with the ability to perform commonsense reasoning has been seen as the bottleneck of artificial general intelligence . |
| Approach: | They propose a textual inference framework that uses external commonsense knowledge graphs to answer commonsensical questions. |
| Outcome: | The proposed framework is based on graph convolutional networks and LSTMs with a hierarchical path-based attention mechanism. |
On the Consistency of Commonsense in Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing evaluations of commonsense for large language models focus on downstream knowledge tasks, failing to probe whether LLMs truly understand and utilize knowledge or merely memorize it. |
| Approach: | They propose to automatically construct a large benchmark named CoCo which measures LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
| Outcome: | The proposed benchmark systematically assesses LLMs’ knowledge memorization, comprehension, and application and examines the consistency between these tasks. |
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
Copied to clipboard
| Challenge: | Workshop on Commonsense Inference in Natural Language Processing focuses on commonsense knowledge representation and application in NLP tasks. |
| Approach: | COIN is a workshop on commonsense inference in natural language processing . workshop included two shared tasks on reading comprehension using commonsensense knowledge . |
| Outcome: | the workshop focused on modeling commonsense knowledge and commonsensing in natural language processing tasks. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Fusing Context Into Knowledge Graph for Commonsense Question Answering (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts. |
| Approach: | They propose to use external entity descriptions to provide contextual information for commonsense question answering models. |
| Outcome: | The proposed model achieves state-of-the-art among non-generative models in OpenBookQA and is the first of its kind in the field. |