PInKS: Preconditioned Commonsense Inference with Minimal Supervision (2022.aacl-main)
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| Challenge: | Existing models for reasoning with preconditions lack data on the problem and lack of support for such reasoning. |
| Approach: | They propose to improve the model for reasoning with preconditions through minimum supervision by PAC-Bayesian informativeness analysis and precision measures. |
| Outcome: | The proposed model improves on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores) it also improves inferences on PAC-Bayesian informativeness analysis, precision measures, and ablation studies. |
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PaCo: Preconditions Attributed to Commonsense Knowledge (2022.findings-emnlp)
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| Challenge: | Existing language models can reason with circumstantial preconditions of commonsense knowledge, but they do not understand the circumstancial precondition. |
| Approach: | They propose to use a dataset to examine the ability of existing language models to understand circumstantial preconditions to improve their reasoning with commonsense knowledge. |
| Outcome: | The proposed task shows that human reasoning with preconditions is an open challenge. |
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. |
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms (2021.emnlp-main)
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| Challenge: | Pre-trained language models have impressive performance on commonsense inference benchmarks, but their ability to make robust inferences is debated. |
| Approach: | They propose a challenge that evaluates robust commonsense inference despite textual perturbations using commonsensical knowledge bases and probe PTLMs across two different evaluation settings. |
| Outcome: | The proposed procedure evaluates robust commonsense inference despite textual perturbations using commonsensense knowledge bases and probe PTLMs across two evaluation settings. |
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models (2021.emnlp-main)
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| Challenge: | Existing language models can be refined for zero-shot commonsense reasoning . however, commons sense reasoning is still an unsolved problem . |
| Approach: | They propose a self-supervised learning approach that refines a pre-trained language model to boost conceptualization. |
| Outcome: | The proposed approach boosts conceptualization by utilizing loss landscape refinement. |
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. |
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. |
Prompting Contrastive Explanations for Commonsense Reasoning Tasks (2021.findings-acl)
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| Challenge: | Large pretrained language models (PLMs) can achieve near-human performance on commonsense reasoning tasks, but provide little human-interpretable evidence of the underlying reasoning they use. |
| Approach: | They propose to use large pretrained language models to generate evidence for commonsense reasoning NLP tasks . they use models to contrast alternative explanations based on key attribute(s) required to justify the correct answer . |
| Outcome: | The proposed model improves performance on two commonsense reasoning benchmarks compared to previous non-contrastive alternatives. |
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. |
| Approach: | They propose to use order-agnostic input to elaborately manipulate the order of the given concepts before generation to evaluate their commonsense knowledge. |
| Outcome: | The proposed approach outperforms more sophisticated models with a lot of external data and resources in the task of generating a logical sentence from a set of concepts. |
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning (2021.emnlp-main)
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| Challenge: | Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream task instances as a language modeling problem. |
| Approach: | They propose to reformulate downstream tasks as a language modeling problem and add a regularization that preserves pretraining weights to the model to mitigate the destructive tendency of few-shot finetuning. |
| Outcome: | The proposed model performs better on low data regimes than the standard model on few-shot finetuning. |