| Challenge: | Existing commonsense reasoning models work by scoring a question-candidate pair, but new approaches are needed to answer multiple-choice questions. |
| Approach: | They propose to use a corpus of commonsense facts to answer a commonsensical question without any pre-defined choices as a resource. |
| Outcome: | The proposed model outperforms baseline methods by a large margin in the open-ended commonsense reasoning task. |
Similar Papers
Open-ended Commonsense Reasoning with Unrestricted Answer Candidates (2023.findings-emnlp)
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Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, Liang Zhao
| Challenge: | Current approaches to commonsense reasoning are limited due to limited answer scope. |
| Approach: | They propose to solve a commonsense question without a pre-defined answer scope . they leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base . |
| Outcome: | The proposed method achieves better performance on two commonsense benchmark datasets. |
Every Answer Matters: Evaluating Commonsense with Probabilistic Measures (2024.acl-long)
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Qi Cheng, Michael Boratko, Pranay Kumar Yelugam, Tim O’Gorman, Nalini Singh, Andrew McCallum, Xiang Li
| Challenge: | Existing commonsense evaluations are often posed as multiple-choice questions, allowing models to exploit systematic biases. |
| Approach: | They propose a generative task that evaluates common sense via multiple open-ended generations and a method that strongly correlates with human judgments. |
| Outcome: | The proposed method outperforms strong language model baselines on a dataset of human and machine common sense. |
Exploring End-to-End Differentiable Natural Logic Modeling (2020.coling-main)
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| Challenge: | Existing approaches to integrate natural logic with neural networks are brittle and prone to fail in the presence of noise and uncertainty. |
| Approach: | They propose to integrate natural logic with neural networks to create differentiable models that integrate natural reasoning with subsymbolic vector representations and neural components. |
| Outcome: | The proposed model can model monotonicity-based reasoning, compared to baseline models without inductive bias. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| 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). |
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference (2021.findings-emnlp)
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| 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. |
Answering Open-Domain Questions of Varying Reasoning Steps from Text (2021.emnlp-main)
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| Challenge: | a new benchmark is developed to answer open-domain questions from text . the system uses a single multi-task transformer model to perform all the necessary subtasks . |
| Approach: | They develop a unified system to answer directly from open-domain questions . they use a single multi-task transformer model to perform all the necessary subtasks . |
| Outcome: | The proposed system can answer open-domain questions on any text collection without prior knowledge of reasoning complexity. |
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. |
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection (2021.emnlp-main)
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| Challenge: | Existing models that handle single-entity questions have focused on relation following . introducing intersection improves performance on multiple-entities questions by over 14% . |
| Approach: | They propose a model that explicitly handles multiple-entity questions by implementing an intersection operation. |
| Outcome: | The proposed model improves on multiple-entity questions by over 14% on two datasets . it also improves performance on questions with multiple entities by 19% . |
LOGICAL-COMMONSENSEQA: A Benchmark for Logical Commonsense Reasoning (2026.acl-short)
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| Challenge: | LOGICAL-COMMONSENSEQA benchmarks evaluate commonsense reasoning as logical composition over pairs of atomic statements . commonsensible reasoning is central to human cognition and a long-standing challenge in artificial intelligence and natural language understanding. |
| Approach: | They propose a benchmark that reframes commonsense reasoning as logical composition over pairs of atomic statements using plausibility-level operators. |
| Outcome: | LOGICAL-COMMONSENSEQA exposes fundamental reasoning limitations and provides a framework for advancing compositional commonsense reasoning. |
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent work proposes lightweight updates to improve commonsense reasoning models . fine-tuning can cause models to overfit to task-specific data and forget knowledge gained during training . |
| Approach: | They propose to use lightweight models to update pre-trained language models to learn commonsense background knowledge. |
| Outcome: | The proposed models learn from commonsense reasoning datasets, but they are overfitted and limited generalized. |