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        <title>Explainable Event Detection with Event Trigger Identification as Rationale Extraction</title>
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            <namePart type="family">Mitkov</namePart>
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    <abstract>Most event detection methods act at the sentence-level and focus on identifying sentences related to a particular event. However, identifying certain parts of a sentence that act as event triggers is also important and more challenging, especially when dealing with limited training data. Previous event detection attempts have considered these two tasks separately and have developed different methods. We hypothesise that similar to humans, successful sentence-level event detection models rely on event triggers to predict sentence-level labels. By exploring feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-the-art sentence-level event detection models and show that explanations (i.e. rationales) extracted from these models can indeed be used to detect event triggers. We, therefore, (i) introduce a novel weakly-supervised method for event trigger detection; and (ii) propose to use event triggers as an explainable measure in sentence-level event detection. To the best of our knowledge, this is the first explainable machine learning approach to event trigger identification.</abstract>
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%0 Conference Proceedings
%T Explainable Event Detection with Event Trigger Identification as Rationale Extraction
%A Hettiarachchi, Hansi
%A Ranasinghe, Tharindu
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F hettiarachchi-ranasinghe-2023-explainable
%X Most event detection methods act at the sentence-level and focus on identifying sentences related to a particular event. However, identifying certain parts of a sentence that act as event triggers is also important and more challenging, especially when dealing with limited training data. Previous event detection attempts have considered these two tasks separately and have developed different methods. We hypothesise that similar to humans, successful sentence-level event detection models rely on event triggers to predict sentence-level labels. By exploring feature attribution methods that assign relevance scores to the inputs to explain model predictions, we study the behaviour of state-of-the-art sentence-level event detection models and show that explanations (i.e. rationales) extracted from these models can indeed be used to detect event triggers. We, therefore, (i) introduce a novel weakly-supervised method for event trigger detection; and (ii) propose to use event triggers as an explainable measure in sentence-level event detection. To the best of our knowledge, this is the first explainable machine learning approach to event trigger identification.
%U https://aclanthology.org/2023.ranlp-1.56
%P 507-518

Markdown (Informal)

[Explainable Event Detection with Event Trigger Identification as Rationale Extraction](https://aclanthology.org/2023.ranlp-1.56) (Hettiarachchi & Ranasinghe, RANLP 2023)

• Explainable Event Detection with Event Trigger Identification as Rationale Extraction (Hettiarachchi & Ranasinghe, RANLP 2023)

ACL

• Hansi Hettiarachchi and Tharindu Ranasinghe. 2023. Explainable Event Detection with Event Trigger Identification as Rationale Extraction. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 507–518, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.