Papers with Commonsense Inference
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing (D19-60)
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| 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. |
CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm (2023.findings-eacl)
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| Challenge: | Existing commonsense reasoning datasets target different knowledge types, modalities, and formats, but how to help machines acquire and infer over commonsensical knowledge is still unclear. |
| Approach: | They propose a commonsense reasoning benchmark to motivate commonsensing progress from two perspectives: (1) Evaluating whether models can distinguish knowledge quality by predicting if the knowledge is enough to answer the question or not. |
| Outcome: | The proposed model outperforms existing models in evaluating their generalization capabilities across tasks while demonstrating that distinguishing knowledge quality remains challenging for current models. |
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense (D19-60)
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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. |
| Approach: | They propose a model that uses Bidirectional Encoder Representations from Transformers and ConceptNet to tackle the problem of commonsense inference in natural language processing. |
| Outcome: | The proposed model achieves 73.3 % accuracy on the official test data. |
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
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| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations (D19-60)
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| Challenge: | Experimental results show that our system achieves significant improvements over the baseline systems with 84.2% accuracy on the official test dataset. |
| Approach: | They propose a system to inject more external knowledge into everyday narrations . they use a pre-trained BERT model to fine-tune on a machine reading comprehension dataset . |
| Outcome: | The proposed system achieves significant improvements over baseline systems with 84.2% accuracy on the official test dataset. |