COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
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| 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. |
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| Challenge: | Existing studies show that pre-trained language models lack commonsense knowledge . |
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| Challenge: | Existing models use commonsense knowledge graphs to extract subgraphs of relevant knowledge pertaining to concepts in the input but due to the large coverage and vast scale of ConceptNet, the extracted subgraph may contain loosely related, redundant and irrelevant information. |
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| Challenge: | Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning. |
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Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
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Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion (2021.tacl-1)
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| Challenge: | Existing approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an ability to generate unobvious concepts. |
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| Challenge: | Using Bidirectional Encoder Representations from Transformers(BERT) and external relational knowledge from ConceptNet, we are able to achieve an accuracy of 73.3 % on the official test data. |
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Enhancing Natural Language Representation with Large-Scale Out-of-Domain Commonsense (2022.findings-acl)
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| Challenge: | Using commonsense in text understanding tasks can cause catastrophic forgetting due to domain discrepancy . previous methods of using textual descriptions as extra input information cannot apply to large-scale commonsensing. |
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Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations (D19-60)
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| Challenge: | Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of their capabilities. |
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Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
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Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach (2022.findings-naacl)
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| Challenge: | Existing approaches to generative commonsense reasoning hypothesize that pre-trained models lack sufficient parametric knowledge for this task. |
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