Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning

TL;DR

This paper enhances Portuguese Semantic Role Labeling by leveraging Transformer models and transfer learning, achieving significant performance improvements over previous methods, especially in low-resource settings.

Contribution

It introduces a simple Transformer-based architecture combined with transfer learning techniques to improve Portuguese SRL performance substantially.

Findings

Over 15 F1 points improvement in Portuguese SRL

Cross-lingual transfer learning boosts results

Transfer from dependency parsing further enhances performance

Abstract

The Natural Language Processing task of determining "Who did what to whom" is called Semantic Role Labeling. For English, recent methods based on Transformer models have allowed for major improvements in this task over the previous state of the art. However, for low resource languages, like Portuguese, currently available semantic role labeling models are hindered by scarce training data. In this paper, we explore a model architecture with only a pre-trained Transformer-based model, a linear layer, softmax and Viterbi decoding. We substantially improve the state-of-the-art performance in Portuguese by over 15 F1. Additionally, we improve semantic role labeling results in Portuguese corpora by exploiting cross-lingual transfer learning using multilingual pre-trained models, and transfer learning from dependency parsing in Portuguese, evaluating the various proposed approaches empirically.