CosMo: Conditional SEQ2SEQ-based Mixture model for zero-shot commonsense question answering

Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Mahsa Baktashmotlagh, Reza Haffari

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)

Abstract

Commonsense reasoning refers to the ability of evaluating a social situation and acting accordingly. Identification of the implicit causes and effects of a social context is the driving capability which can enable machines to perform commonsense reasoning. The dynamic world of social interactions requires context-dependent on-demand systems to infer such underlying information. However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations. Hence they fail to estimate the correct reasoning path. In this paper, we present Conditional SEQ2SEQ-based Mixture model (COSMO), which provides us with the capabilities of dynamic and diverse content generation. We use COSMO to generate context-dependent clauses, which form a dynamic Knowledge Graph (KG) on-the-fly for commonsense reasoning. To show the adaptability of our model to context-dependant knowledge generation, we address the task of zero-shot commonsense question answering. The empirical results indicate an improvement of up to +5.2% over the state-of-the-art models.

Original languageEnglish
Title of host publicationCOLING 2020
Subtitle of host publicationThe 28th International Conference on Computational Linguistics, Proceedings of the Conference
EditorsNuria Bel, Chengquing Zong
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Pages5347-5359
Number of pages13
ISBN (Electronic)9781952148279
DOIs
Publication statusPublished - 2020
EventInternational Conference on Computational Linguistics 2020 - Virtual, Barcelona, Spain
Duration: 8 Dec 202013 Dec 2020
Conference number: 28th
https://coling2020.org (Website)
https://www.aclweb.org/anthology/volumes/2020.coling-main/ (Proceedings)

Conference

ConferenceInternational Conference on Computational Linguistics 2020
Abbreviated titleCOLING 2020
Country/TerritorySpain
CityBarcelona
Period8/12/2013/12/20
Internet address

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