@inproceedings{klein-etal-2022-qasem,
title = "{QAS}em Parsing: Text-to-text Modeling of {QA}-based Semantics",
author = "Klein, Ayal and
Hirsch, Eran and
Eliav, Ron and
Pyatkin, Valentina and
Caciularu, Avi and
Dagan, Ido",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.528",
doi = "10.18653/v1/2022.emnlp-main.528",
pages = "7742--7756",
abstract = "Various works suggest the appeals of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios. Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides a shallow and partial representation. More recently, an appealing trend introduces semi-structured natural-language structures as an intermediate meaning-capturing representation, often in the form of questions and answers. In this work, we further promote this line of research by considering three prior QA-based semantic representations. These cover verbal, nominalized and discourse-based predications, regarded as jointly providing a comprehensive representation of textual information {---} termed QASem. To facilitate this perspective, we investigate how to best utilize pre-trained sequence-to-sequence language models, which seem particularly promising for generating representations that consist of natural language expressions (questions and answers). In particular, we examine and analyze input and output linearization strategies, as well as data augmentation and multitask learning for a scarce training data setup. Consequently, we release the first unified QASem parsing tool, easily applicable for downstream tasks that can benefit from an explicit semi-structured account of information units in text.",
}
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<abstract>Various works suggest the appeals of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios. Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides a shallow and partial representation. More recently, an appealing trend introduces semi-structured natural-language structures as an intermediate meaning-capturing representation, often in the form of questions and answers. In this work, we further promote this line of research by considering three prior QA-based semantic representations. These cover verbal, nominalized and discourse-based predications, regarded as jointly providing a comprehensive representation of textual information — termed QASem. To facilitate this perspective, we investigate how to best utilize pre-trained sequence-to-sequence language models, which seem particularly promising for generating representations that consist of natural language expressions (questions and answers). In particular, we examine and analyze input and output linearization strategies, as well as data augmentation and multitask learning for a scarce training data setup. Consequently, we release the first unified QASem parsing tool, easily applicable for downstream tasks that can benefit from an explicit semi-structured account of information units in text.</abstract>
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%0 Conference Proceedings
%T QASem Parsing: Text-to-text Modeling of QA-based Semantics
%A Klein, Ayal
%A Hirsch, Eran
%A Eliav, Ron
%A Pyatkin, Valentina
%A Caciularu, Avi
%A Dagan, Ido
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F klein-etal-2022-qasem
%X Various works suggest the appeals of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios. Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides a shallow and partial representation. More recently, an appealing trend introduces semi-structured natural-language structures as an intermediate meaning-capturing representation, often in the form of questions and answers. In this work, we further promote this line of research by considering three prior QA-based semantic representations. These cover verbal, nominalized and discourse-based predications, regarded as jointly providing a comprehensive representation of textual information — termed QASem. To facilitate this perspective, we investigate how to best utilize pre-trained sequence-to-sequence language models, which seem particularly promising for generating representations that consist of natural language expressions (questions and answers). In particular, we examine and analyze input and output linearization strategies, as well as data augmentation and multitask learning for a scarce training data setup. Consequently, we release the first unified QASem parsing tool, easily applicable for downstream tasks that can benefit from an explicit semi-structured account of information units in text.
%R 10.18653/v1/2022.emnlp-main.528
%U https://aclanthology.org/2022.emnlp-main.528
%U https://doi.org/10.18653/v1/2022.emnlp-main.528
%P 7742-7756
Markdown (Informal)
[QASem Parsing: Text-to-text Modeling of QA-based Semantics](https://aclanthology.org/2022.emnlp-main.528) (Klein et al., EMNLP 2022)
ACL
- Ayal Klein, Eran Hirsch, Ron Eliav, Valentina Pyatkin, Avi Caciularu, and Ido Dagan. 2022. QASem Parsing: Text-to-text Modeling of QA-based Semantics. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7742–7756, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.