@inproceedings{becker-etal-2021-co,
title = "{CO}-{NNECT}: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts",
author = "Becker, Maria and
Korfhage, Katharina and
Paul, Debjit and
Frank, Anette",
editor = "Zarrie{\ss}, Sina and
Bos, Johan and
van Noord, Rik and
Abzianidze, Lasha",
booktitle = "Proceedings of the 14th International Conference on Computational Semantics (IWCS)",
month = jun,
year = "2021",
address = "Groningen, The Netherlands (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwcs-1.3",
pages = "21--32",
abstract = "In this work we leverage commonsense knowledge in form of knowledge paths to establish connections between sentences, as a form of explicitation of implicit knowledge. Such connections can be direct (singlehop paths) or require intermediate concepts (multihop paths). To construct such paths we combine two model types in a joint framework we call Co-nnect: a relation classifier that predicts direct connections between concepts; and a target prediction model that generates target or intermediate concepts given a source concept and a relation, which we use to construct multihop paths. Unlike prior work that relies exclusively on static knowledge sources, we leverage language models finetuned on knowledge stored in ConceptNet, to dynamically generate knowledge paths, as explanations of implicit knowledge that connects sentences in texts. As a central contribution we design manual and automatic evaluation settings for assessing the quality of the generated paths. We conduct evaluations on two argumentative datasets and show that a combination of the two model types generates meaningful, high-quality knowledge paths between sentences that reveal implicit knowledge conveyed in text.",
}
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%0 Conference Proceedings
%T CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts
%A Becker, Maria
%A Korfhage, Katharina
%A Paul, Debjit
%A Frank, Anette
%Y Zarrieß, Sina
%Y Bos, Johan
%Y van Noord, Rik
%Y Abzianidze, Lasha
%S Proceedings of the 14th International Conference on Computational Semantics (IWCS)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Groningen, The Netherlands (online)
%F becker-etal-2021-co
%X In this work we leverage commonsense knowledge in form of knowledge paths to establish connections between sentences, as a form of explicitation of implicit knowledge. Such connections can be direct (singlehop paths) or require intermediate concepts (multihop paths). To construct such paths we combine two model types in a joint framework we call Co-nnect: a relation classifier that predicts direct connections between concepts; and a target prediction model that generates target or intermediate concepts given a source concept and a relation, which we use to construct multihop paths. Unlike prior work that relies exclusively on static knowledge sources, we leverage language models finetuned on knowledge stored in ConceptNet, to dynamically generate knowledge paths, as explanations of implicit knowledge that connects sentences in texts. As a central contribution we design manual and automatic evaluation settings for assessing the quality of the generated paths. We conduct evaluations on two argumentative datasets and show that a combination of the two model types generates meaningful, high-quality knowledge paths between sentences that reveal implicit knowledge conveyed in text.
%U https://aclanthology.org/2021.iwcs-1.3
%P 21-32
Markdown (Informal)
[CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts](https://aclanthology.org/2021.iwcs-1.3) (Becker et al., IWCS 2021)
ACL