@inproceedings{awasthy-etal-2021-ibm,
title = "{IBM} {MNLP} {IE} at {CASE} 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News",
author = "Awasthy, Parul and
Ni, Jian and
Barker, Ken and
Florian, Radu",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali},
booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.case-1.18/",
doi = "10.18653/v1/2021.case-1.18",
pages = "138--146",
abstract = "In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021. The shared task has 4 subtasks which cover event detection at different granularity levels (from document level to token level) and across multiple languages (English, Hindi, Portuguese and Spanish). To handle data from multiple languages, we use a multilingual transformer-based language model (XLM-R) as the input text encoder. We apply a variety of techniques and build several transformer-based models that perform consistently well across all the subtasks and languages. Our systems achieve an average F{\_}1 score of 81.2. Out of thirteen subtask-language tracks, our submissions rank 1st in nine and 2nd in four tracks."
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%0 Conference Proceedings
%T IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News
%A Awasthy, Parul
%A Ni, Jian
%A Barker, Ken
%A Florian, Radu
%Y Hürriyetoğlu, Ali
%S Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F awasthy-etal-2021-ibm
%X In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021. The shared task has 4 subtasks which cover event detection at different granularity levels (from document level to token level) and across multiple languages (English, Hindi, Portuguese and Spanish). To handle data from multiple languages, we use a multilingual transformer-based language model (XLM-R) as the input text encoder. We apply a variety of techniques and build several transformer-based models that perform consistently well across all the subtasks and languages. Our systems achieve an average F_1 score of 81.2. Out of thirteen subtask-language tracks, our submissions rank 1st in nine and 2nd in four tracks.
%R 10.18653/v1/2021.case-1.18
%U https://aclanthology.org/2021.case-1.18/
%U https://doi.org/10.18653/v1/2021.case-1.18
%P 138-146
Markdown (Informal)
[IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News](https://aclanthology.org/2021.case-1.18/) (Awasthy et al., CASE 2021)
ACL