# Priberam Labs at the NTCIR-15 SHINRA2020-ML: Classification Task

arXiv: https://arxiv.org/abs/2105.05605 · HTML: https://mlsanity.com/paper/2105.05605/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 12 May 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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [A Comparison of LSTM and BERT for Small Corpus](https://mlsanity.com/paper/2009.05451/) (2020)
- [MultiFiT: Efficient Multi-lingual Language Model Fine-tuning](https://mlsanity.com/paper/1909.04761/) (2019)
- [Polyglot: Distributed Word Representations for Multilingual NLP](https://mlsanity.com/paper/1307.1662/) (2013)
- [Unsupervised Multilingual Word Embeddings](https://mlsanity.com/paper/1808.08933/) (2018)
- [Light-Weighted CNN for Text Classification](https://mlsanity.com/paper/2004.07922/) (2020)

## Abstract (arXiv)

Wikipedia is an online encyclopedia available in 285 languages. It composes an extremely relevant Knowledge Base (KB), which could be leveraged by automatic systems for several purposes. However, the structure and organisation of such information are not prone to automatic parsing and understanding and it is, therefore, necessary to structure this knowledge. The goal of the current SHINRA2020-ML task is to leverage Wikipedia pages in order to categorise their corresponding entities across 268 hierarchical categories, belonging to the Extended Named Entity (ENE) ontology. In this work, we propose three distinct models based on the contextualised embeddings yielded by Multilingual BERT. We explore the performances of a linear layer with and without explicit usage of the ontology's hierarchy, and a Gated Recurrent Units (GRU) layer. We also test several pooling strategies to leverage BERT's embeddings and selection criteria based on the labels' scores. We were able to achieve good performance across a large variety of languages, including those not seen during the fine-tuning process (zero-shot languages).