Commonsense Reasoning for Natural Language Processing

Sap, Maarten; Shwartz, Vered; Bosselut, Antoine; Choi, Yejin; Roth, Dan

doi:10.18653/v1/2020.acl-tutorials.7

Cited by 60 publications

(50 citation statements)

References 51 publications

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“…However, these tasks are currently under investigation and the field calls for major breakthroughs. For more information, we refer to a recent survey (Storks et al, 2019) and a tutorial (Sap et al, 2020).…”

Section: Commonsense Reasoningmentioning

confidence: 99%

Automated fact‐checking: A survey

Zeng

Abumansour

Zubiaga

2021

Language and Linguist. Compass

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As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐checking covering both the claim detection and claim validation components.

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Section: Commonsense Reasoningmentioning

confidence: 99%

Automated fact‐checking: A survey

Zeng

Abumansour

Zubiaga

2021

Language and Linguist. Compass

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“…Commonsense knowledge refers to the necessary level of practical knowledge and reasoning about everyday situations and events common among most people (Sap et al, 2020). For example, one should know that "water is for drinking" and "sunshine makes people warm".…”

Section: Commonsense Knowledgementioning

confidence: 99%

KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

Xing¹,

Shi²,

Zeng³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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We present Knowledge Enhanced Multimodal BART (KM-BART), which is a Transformerbased sequence-to-sequence model capable of reasoning about commonsense knowledge from multimodal inputs of images and texts. We adapt the generative BART architecture (Lewis et al., 2020) to a multimodal model with visual and textual inputs. We further develop novel pretraining tasks to improve the model performance on the Visual Commonsense Generation (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Generation (KCG) boosts model performance on the VCG task by leveraging commonsense knowledge from a large language model pretrained on external commonsense knowledge graphs. To the best of our knowledge, we are the first to propose a dedicated task for improving model performance on the VCG task. Experimental results show that our model reaches state-of-the-art performance on the VCG task (Park et al., 2020) by applying these novel pretraining tasks.

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“…Stage 3: Common sense: After establishing consistency of the concept, a human-like interaction additionally calls for grounding the common sense associated with the concept in that scenario. In addition to the basic level of practical knowledge that concerns with day to day scenarios Sap et al (2020), the concept should also be reasoned based on that particular context. This contextual common sense moves the idiosyncratic sense towards a sense of collective understanding.…”

Section: Dimension 2: Purviews Of Groundingmentioning

confidence: 99%

Grounding ‘Grounding’ in NLP

Chandu

Bisk

Black

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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The NLP community has seen substantial recent interest in grounding to facilitate interaction between language technologies and the world. However, as a community, we use the term broadly to reference any linking of text to data or non-textual modality. In contrast, Cognitive Science more formally defines "grounding" as the process of establishing what mutual information is required for successful communication between two interlocutorsa definition which might implicitly capture the NLP usage but differs in intent and scope.We investigate the gap between these definitions and seek answers to the following questions: (1) What aspects of grounding are missing from NLP tasks? Here we present the dimensions of coordination, purviews and constraints.(2) How is the term "grounding" used in the current research? We study the trends in datasets, domains, and tasks introduced in recent NLP conferences. And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science? We present ways to both create new tasks or repurpose existing ones to make advancements towards achieving a more complete sense of grounding.

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Commonsense Reasoning for Natural Language Processing

Cited by 60 publications

References 51 publications

Automated fact‐checking: A survey

Automated fact‐checking: A survey

KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation

Grounding ‘Grounding’ in NLP

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