MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2286 - 2299 July 10-15, 2022 ©2022 Association for Computational Linguistics MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection Amir Pouran Ben Veyseh1, Minh Van Nguyen1, Franck Dernoncourt2, and Thien Huu Nguyen1 1 Dept. of Computer and Information Science, University of Oregon, Eugene, OR, USA 2 Adobe Research, Seattle, WA, USA {apouranb,minhnv,thien}@cs.uoregon.edu, dernonco@adobe.com Abstract Event Detection (ED) is the task of identify- ing and classifying trigger words of event men- tions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English lan- guages, important research questions for ED include how well existing ED models perform on different languages, how challenging ED is in other languages, and how well ED knowl- edge and annotation can be transferred across languages. To answer those questions, it is cru- cial to obtain multilingual ED datasets that pro- vide consistent event annotation for multiple languages. There exist some multilingual ED datasets; however, they tend to cover a hand- ful of languages and mainly focus on popular ones. Many languages are not covered in exist- ing multilingual ED datasets. In addition, the current datasets are often small and not acces- sible to the public. To overcome those short- comings, we introduce a new large-scale mul- tilingual dataset for ED (called MINION) that consistently annotates events for 8 different lan- guages; 5 of them have not been supported by existing multilingual datasets. We also perform extensive experiments and analysis to demon- strate the challenges and transferability of ED across languages in MINION that in all call for more research effort in this area. We will release the dataset to promote future research on multilingual ED. 1 Introduction Event Detection (ED) is one of the critical steps for an Event Extraction system in Information Ex- traction (IE) that aims is to recognize mentions of events in text, i.e., change of state of real world entities. Specifically, an ED system identifies the word(s) that most clearly refer to the occurrence of an event, i.e., event trigger, and also detects the type of event that is evoked by the event trigger. For instance, in the sentence “The city was reportedly struck by F16 missiles.”, the word “struck” is the trigger for an ATTACK event. An ED model can be incorporated into other IE pipelines to facilitate the extraction of information related to events and entities, thereby supporting various downstream applications such as knowledge base construction, question answering and text summarization. Due to its importance, ED has been extensively studied in the IE and NLP community over the past decade. Existing methods for ED extend from feature-based models (Ahn, 2006; Liao and Grish- man, 2010; Miwa et al., 2014a), to advanced deep learning methods (Nguyen and Grishman, 2015; Chen et al., 2015; Sha et al., 2018; Wang et al., 2019; Yang et …

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