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    <abstract>Automated discovery of causal relationships from text is a challenging task. Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training. To overcome this limitation, in this paper, we leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation. The term adaptive is used since the training and test data come from two distributionally different datasets, which to the best of our knowledge, this work is the first to address. Moreover, we present a new causality dataset, namely MedCaus, which integrates all types of causality in the text. Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7% improvement, on the tasks of identification and localisation of the causal relations from the text.</abstract>
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%0 Conference Proceedings
%T Domain Adaptative Causality Encoder
%A Moghimifar, Farhad
%A Haffari, Gholamreza
%A Baktashmotlagh, Mahsa
%Y Kim, Maria
%Y Beck, Daniel
%Y Mistica, Meladel
%S Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association
%D 2020
%8 December
%I Australasian Language Technology Association
%C Virtual Workshop
%F moghimifar-etal-2020-domain
%X Automated discovery of causal relationships from text is a challenging task. Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training. To overcome this limitation, in this paper, we leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation. The term adaptive is used since the training and test data come from two distributionally different datasets, which to the best of our knowledge, this work is the first to address. Moreover, we present a new causality dataset, namely MedCaus, which integrates all types of causality in the text. Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7% improvement, on the tasks of identification and localisation of the causal relations from the text.
%U https://aclanthology.org/2020.alta-1.1
%P 1-10

Markdown (Informal)

[Domain Adaptative Causality Encoder](https://aclanthology.org/2020.alta-1.1) (Moghimifar et al., ALTA 2020)

• Domain Adaptative Causality Encoder (Moghimifar et al., ALTA 2020)

ACL

• Farhad Moghimifar, Gholamreza Haffari, and Mahsa Baktashmotlagh. 2020. Domain Adaptative Causality Encoder. In Proceedings of the 18th Annual Workshop of the Australasian Language Technology Association, pages 1–10, Virtual Workshop. Australasian Language Technology Association.