| Challenge: | a hybrid neural network (HNN) model for commonsense reasoning is proposed . it combines language models and semantic similarity models to achieve new state-of-the-art results . |
| Approach: | They propose a hybrid neural network model for commonsense reasoning . it combines a masked language model and a semantic similarity model . |
| Outcome: | The proposed model outperforms the WNLI, WSC and PDP60 benchmarks on three commonsense reasoning tasks. |
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Unsupervised Deep Structured Semantic Models for Commonsense Reasoning (N19-1)
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| Challenge: | Existing methods for commonsense reasoning rely on human-crafted features and knowledge bases, but unsupervised learning is not feasible due to the lack of labeled training data or comprehensive knowledge bases. |
| Approach: | They propose two unsupervised models based on the Deep Structured Semantic Models framework to tackle two commonsense reasoning tasks: Winograd Schema Challenge (WSC) and Pronoun Disambiguation (PDP). |
| Outcome: | The proposed models capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches. |
ACENet: Attention Guided Commonsense Reasoning on Hybrid Knowledge Graph (2022.emnlp-main)
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| Challenge: | Existing approaches estimate plausibility of candidate choices separately based on their respective KGs, without considering the interference among different choices. |
| Approach: | They propose an Attention guided Commonsense rEasoning Network to integrate hybrid knowledge into the neural network. |
| Outcome: | The proposed model outperforms existing methods on CommonsenseQA and OpenbookQA datasets and shows significant performance gains. |
Learning Contextualized Knowledge Structures for Commonsense Reasoning (2021.findings-acl)
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Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
| Challenge: | Recent knowledge graph (KG) augmented models have achieved notable success on commonsense reasoning tasks. |
| Approach: | They propose a KG-augmented model that contextualizes extracted and generated knowledge by reasoning over both within a single graph structure. |
| Outcome: | The proposed model outperforms existing models on four commonsense reasoning benchmarks and a user study on edge validness and helpfulness. |
Great~Truths~are ~Always ~Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models (2022.findings-naacl)
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| 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. |
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language (P19-1)
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| Challenge: | ambiguity in natural language is difficult to interpret due to large linguistic variability. |
| Approach: | They propose to use a Prolog prover to extend neural networks with logic programming to solve multi-hop reasoning tasks over natural language. |
| Outcome: | The proposed model outperforms baseline models on two question answering tasks and is competitive on the MedHop corpus. |
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | a test suite to evaluate commonsense reasoning capability of neural machine translation is presented . language models pretrained on large-scale corpora achieve a commonsensing accuracy of lower than 72% on target translations of this test suite. |
| Approach: | They propose a test suite to evaluate the commonsense reasoning capability of neural machine translation. |
| Outcome: | The proposed test suite performs poorly on commonsense reasoning of the three ambiguity types in terms of reasoning accuracy and reasoning consistency. |
Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
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| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
| Approach: | They propose to use commonsense auto-generated explanations to train language models to generate explanations that can be used during training and inference in a commonsensense Auto-Generated Explanation framework. |
| Outcome: | Empirical results show that the proposed framework improves on the commonsenseQA task by 10%. |
Neural-Symbolic Commonsense Reasoner with Relation Predictors (2021.acl-short)
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| Challenge: | Existing models for commonsense reasoning are limited by their limited set of facts, rendering them unfit for reasoning over new unseen situations and events. |
| Approach: | They propose a neural-symbolic reasoner which can combine commonsense facts with large-scale dynamic CKGs to draw conclusions about ordinary situations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the task of link prediction on CKGs. |
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. |
Contrastive Self-Supervised Learning for Commonsense Reasoning (2020.acl-main)
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| Challenge: | Existing methods for commonsense reasoning are limited by current methods . empirical results show that our method alleviates the limitation of current supervised approaches . |
| Approach: | They propose a self-supervised method to solve pronoun disambiguation problems . they leverage a mutual exclusive loss regularized by a contrastive margin to achieve commonsense reasoning . |
| Outcome: | The proposed method performs well on many NLP benchmarks. |