• Corpus ID: 222310337

COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

@inproceedings{Hwang2021COMETATOMIC2O,
  title={COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs},
  author={Jena D. Hwang and Chandra Bhagavatula and Ronan Le Bras and Jeff Da and Keisuke Sakaguchi and Antoine Bosselut and Yejin Choi},
  booktitle={AAAI},
  year={2021}
}
Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs (CSKG) has been central to these advances as their diverse facts can be used and referenced by machine learning models for tackling new and challenging tasks. At the same time, there remain questions about the quality and coverage of these resources due to the massive scale required to comprehensively… 
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90 Citations
Highly Influential Citations
25
Background Citations
64
Methods Citations
38
Results Citations
1

90 Citations

Citation Type

Citation Type

Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
Reasoning over commonsense knowledge bases (CSKB) whose elements are in the form of free-text is an important yet hard task in NLP. While CSKB completion only fills the missing links within the…
Analyzing Commonsense Emergence in Few-shot Knowledge Models
TLDR
The results show that commonsense knowledge models can rapidly adapt from limited examples, indicating that KG fine-tuning serves to learn an interface to encoded knowledge learned during pretraining.
Understanding Few-Shot Commonsense Knowledge Models
TLDR
This work investigates training commonsense knowledge models in a fewshot setting with limited tuples per commonsense relation in the graph and finds that human quality ratings for knowledge produced from a few-shot trained system can achieve performance within 6% of knowledgeproduced from fully supervised systems.
BertNet: Harvesting Knowledge Graphs from Pretrained Language Models
TLDR
This work aims at harvesting symbolic KGs from the LMs, a new framework for automatic KG construction empowered by the neural LMs’ flexibility and scalability, and derives from diverse LMs a family of new KGs that contain a richer set of commonsense relations, including complex ones than the human-annotated KGs.
Commonsense Knowledge in Word Associations and ConceptNet
TLDR
An in-depth comparison of two large-scale resources of general knowledge: ConceptNet, an engineered relational database, and SWOW, a knowledge graph derived from crowd-sourced word associations shows empirically that both resources improve downstream task performance on commonsense reasoning benchmarks over text-only baselines.
DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge
TLDR
Experiments demonstrate that the proposed commonsense knowledge acquisition framework DISCOS can successfully convert discourse knowledge about eventualities from ASER, a large-scale discourse knowledge graph, into if-then Commonsense knowledge defined in ATOMIC without any additional annotation effort.
Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
TLDR
This work thoroughly study the possible role of conceptualization in commonsense reasoning, formulates a framework to replicate human conceptual induction from acquiring abstract knowledge about abstract concepts, and develops tools for contextualization on ATOMIC, a large-scale human annotated CKG.
Commonsense Reasoning: \protect \@normalcr how do Neuro-Symbolic and Neuro-only approaches compare?
TLDR
This paper sets out to compare a Neuro-Symbolic model with mainstream Neuro-only models when they are tasked with solving commonsense reasoning problems, and indicates that there is no clear advantage to either approach.
Commonsense Reasoning: How do Neuro-Symbolic and Neuro-only Approaches Compare?
TLDR
This paper sets out to compare a Neuro-Symbolic model with mainstream Neuro-only models when they are tasked with solving commonsense reasoning problems, and indicates that there is no clear advantage to either approach.
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference
TLDR
This work shows the effectiveness of using a common framework, Natural Language Inference (NLI), to solve diverse commonsense reasoning tasks, by leveraging transfer learning from large NLI datasets, and injecting crucial knowledge from commonsense sources such as ATOMIC 2020 and ConceptNet.
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References

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