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AMU-EURANOVA at CASE 2021 Task 1: Assessing the stability of multilingual BERT

arXiv 2106.14625 ↗First posted 10 Jun 2021cs.CL cs.AIcited by 0

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Abstract (arXiv)

This paper explains our participation in task 1 of the CASE 2021 shared task. This task is about multilingual event extraction from news. We focused on sub-task 4, event information extraction. This sub-task has a small training dataset and we fine-tuned a multilingual BERT to solve this sub-task. We studied the instability problem on the dataset and tried to mitigate it.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 10 Jun 2021.

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References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  3. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  4. 2019How multilingual is Multilingual BERT?cited by 645
  5. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  6. 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 384
  7. 2020On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselinescited by 255
  8. 2020Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linkingcited by 49
  9. 2020Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Reportcited by 12
  10. 2020Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Settingcited by 8

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