mlsanityData to 14 Oct 2026

PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguese

arXiv 2404.05333 ↗First posted 8 Apr 2024cs.CLcited by 5

Not on the map. Why?

Abstract (arXiv)

Leveraging research on the neural modelling of Portuguese, we contribute a collection of datasets for an array of language processing tasks and a corresponding collection of fine-tuned neural language models on these downstream tasks. To align with mainstream benchmarks in the literature, originally developed in English, and to kick start their Portuguese counterparts, the datasets were machine-translated from English with a state-of-the-art translation engine. The resulting PORTULAN ExtraGLUE benchmark is a basis for research on Portuguese whose improvement can be pursued in future work. Similarly, the respective fine-tuned neural language models, developed with a low-rank adaptation approach, are made available as baselines that can stimulate future work on the neural processing of Portuguese. All datasets and models have been developed and are made available for two variants of Portuguese: European and Brazilian.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 39 of its 62 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

39 of 62 references.

References · 39

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  7. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  8. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  9. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  10. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  11. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,183
  12. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
27 more
  1. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  2. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,729
  3. 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 1,001
  4. 2018Neural Network Acceptability Judgmentscited by 958
  5. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  6. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 742
  7. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  8. 2021Compacter: Efficient Low-Rank Hypercomplex Adapter Layerscited by 460
  9. 2018Gender Bias in Coreference Resolutioncited by 442
  10. 2018WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representationscited by 372
  11. 2020XCOPA: A Multilingual Dataset for Causal Commonsense Reasoningcited by 333
  12. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 271
  13. 2019PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identificationcited by 264
  14. 2022DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptationcited by 233
  15. 2020XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generationcited by 222
  16. 2021Gender Bias in Machine Translationcited by 117
  17. 2020Translation Artifacts in Cross-lingual Transfer Learningcited by 96
  18. 2019Evaluating Commonsense in Pre-trained Language Modelscited by 95
  19. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 72
  20. 2020FQuAD: French Question Answering Datasetcited by 58
  21. 2020RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmarkcited by 53
  22. 2019Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answeringcited by 35
  23. 2022Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translationcited by 28
  24. 2023Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*cited by 24
  25. 2018Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!cited by 16
  26. 2022Slovene SuperGLUE Benchmark: Translation and Evaluationcited by 7
  27. 2023Challenges in Context-Aware Neural Machine Translationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 2.

5 papers.