Commonsense Reasoning in a Deeper Way: By Discovering Relations between Predicates

Huang, Wenguan; Luo, Xudong

doi:10.5220/0006120504070414

Cited by 4 publications

(5 citation statements)

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“…Other semantic plausibility datasets have collected judgments for the plausibility of single events (Wang et al, 2018b) and the plausibility of adjectives modifying a meronym (Mullenbach et al, 2019). Such plausibility tasks have often been solved using either data-driven methods (Huang and Luo, 2017;Sasaki et al, 2017) or pre-trained LMs .…”

Section: Recognizing Textual Entailment and Semanticmentioning

confidence: 99%

ADEPT: An Adjective-Dependent Plausibility Task

Emami

Porada²,

Olteanu

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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A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors. While correctly interpreting and assessing the effects of such adjective-noun pairs (e.g., false key) on the plausibility of given events (e.g., opening doors) underpins many natural language understanding tasks, doing so often requires a significant degree of world knowledge and common-sense reasoning. We introduce ADEPT -a large-scale semantic plausibility task consisting of over 16 thousand sentences that are paired with slightly modified versions obtained by adding an adjective to a noun. Overall, we find that while the task appears easier for human judges (85% accuracy), it proves more difficult for transformer-based models like RoBERTa (71% accuracy). Our experiments also show that neither the adjective itself nor its taxonomic class suffice in determining the correct plausibility judgement, emphasizing the importance of endowing automatic natural language understanding systems with more context sensitivity and commonsense reasoning.

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Section: Recognizing Textual Entailment and Semanticmentioning

confidence: 99%

ADEPT: An Adjective-Dependent Plausibility Task

Emami

Porada²,

Olteanu

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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“…The selection is based often on knowledge-type constraints. Sharma et al (2015)'s knowledge-hunting module focused on a subset of 71 instances that exhibit causal relationships; Liu et al (2016)'s neural association model focused on a similar causal subset of 70 instances, for which events were extracted manually; and finally, a recent system by Huang and Luo (2017) focused on 49 instances. While these approaches demonstrate that difficult coreference problems can be resolved when they adhere to certain knowledge or structural constraints, they may fail to generalize to other settings.…”

Section: Related Workmentioning

confidence: 99%

A Knowledge Hunting Framework for Common Sense Reasoning

Emami¹,

Cruz²,

Trischler³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. Our method uses a knowledge hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable. 1 a) The man couldn't lift his son because he was so weak. (Answer: the man) 1 b) The man couldn't lift his son because he was so heavy. (Answer: son) 2 a) The older students were bullying the younger ones, so we punished them. (Answer: the older students) 2 b) The older students were bullying the younger ones, so we rescued them. (Answer: the younger ones)

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“…Such method has to take the burden of efficient access to every piece of knowledge which increases with increased data size. Predicate relations can be used to enhance the commonsense reasoning ability [10]. Only few studies have projected the use of predicates in that manner.…”

Section: Knowledge Representation Issuesmentioning

confidence: 99%

Automation of Commonsense Reasoning: A Study on Feasible Knowledge Representation Techniques

Hegde¹

2017

ijarcs

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Commonsense reasoning is a branch of artificial intelligence that deals with the simulation of human ability to make decisions during the situations that we encounter every day. This paper presents a feasibility study of necessary knowledge representation techniques used in commonsense reasoning study. Simulating the reasoning is a very challenging task since an intelligent agent that is ought to implement reasoning is not more than a computer itself. This paper is also intended at projecting some hurdles in the choice of knowledge representation. The diversity of issues that makes commonsense reasoning very hard to implement are definitely not few in number. Hence, the effort is to address only the problem of knowledge representation for commonsense reasoning by considering some of the major representation techniques which is the main point of discussion of this paper.

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Commonsense Reasoning in a Deeper Way: By Discovering Relations between Predicates

Cited by 4 publications

References 0 publications

ADEPT: An Adjective-Dependent Plausibility Task

ADEPT: An Adjective-Dependent Plausibility Task

A Knowledge Hunting Framework for Common Sense Reasoning

Automation of Commonsense Reasoning: A Study on Feasible Knowledge Representation Techniques

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