| Challenge: | Existing work on modeling semantic plausibility has focused on physical plausability but distributional methods fail when tested in supervised settings. |
| Approach: | They propose to use large pretrained language models to model plausibility in supervised settings by extracting attested events from a large corpus and injecting explicit commonsense knowledge into a distributional model. |
| Outcome: | The proposed model is effective in modeling plausibility in a supervised setting. |
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Modeling Semantic Plausibility by Injecting World Knowledge (N18-2)
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| Challenge: | Existing models for semantic plausibility are based on distributional data, but injecting knowledge about entity properties provides a substantial performance boost. |
| Approach: | They propose to inject manually elicited knowledge about entity properties into a dataset to improve plausibility models. |
| Outcome: | The proposed dataset is a great testbed for semantic plausibility models . it shows that injection of knowledge about entity properties improves performance . |
Modeling Event Plausibility with Consistent Conceptual Abstraction (2021.naacl-main)
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| Challenge: | Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. |
| Approach: | They propose a method of forcing model consistency that improves correlation with human plausibility judgements. |
| Outcome: | The proposed method improves correlation with human plausibility judgements. |
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). |
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) are limited in their ability to capture and use common-sense knowledge. |
| Approach: | They propose to teach PLMs how to reason with soft Horn rules by leveraging logical rules to learn how to predict precise probabilities. |
| Outcome: | The proposed model performs well on logical rules that were unseen at training. |
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence (2022.findings-naacl)
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| Challenge: | Recent evidence shows that large-size pre-trained language models do not satisfy the logical negation property (LNP) However, their reliability is being challenged due to faulty behaviours and incomprehension on number-related representations. |
| Approach: | They propose a new intermediate training task to directly learn meaning text correspondence instead of relying on the distributional hypothesis. |
| Outcome: | The proposed approach outperforms previous models on 7 GLUE tasks and outperformed previous models. |
Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
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. |
Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution (2023.findings-emnlp)
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| Challenge: | a new approach to train, evaluate and interpret neural language models uses artificial, language-like data. |
| Approach: | They propose a setup for training, evaluating and interpreting neural language models that uses artificial, language-like data. |
| Outcome: | The proposed model is based on a massive probabilistic grammar and a large natural language corpus, and provides complete control over the generative process. |
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. |
Probing Pre-trained Language Models for Semantic Attributes and their Values (2021.findings-emnlp)
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| Challenge: | Pretrained language models (PTLMs) are used for many tasks including syntax, semantics and commonsense. |
| Approach: | They propose to integrate semantic attributes and their values into pretrained language models to improve their performance on many natural language processing tasks. |
| Outcome: | The proposed model performs better on masked tokens than humans on this task. |