# Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks

arXiv: https://arxiv.org/abs/2110.15599 · HTML: https://mlsanity.com/paper/2110.15599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 18 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://mlsanity.com/paper/1808.09861/) (2018)
- [UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data](https://mlsanity.com/paper/2007.07683/) (2020)
- [Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report](https://mlsanity.com/paper/2005.06070/) (2020)
- [Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Setting](https://mlsanity.com/paper/2008.00345/) (2020)

## Abstract (arXiv)

The aim of the CASE 2021 Shared Task 1 (H"urriyetouglu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language. Our submission contained entries in all of the subtasks, and the scores obtained validated our research finding: That the multilingual aspect of the tasks should be embraced, so that modeling and training regimes use the multilingual nature of the tasks to their mutual benefit, rather than trying to tackle the different languages separately. Our code is available at https://github.com/HandshakesByDC/case2021/