Persian Semantic Role Labeling Using Transfer Learning and BERT-Based
  Models
Saeideh Niksirat Aghdam Sayyed Ali Hossayni Erfan Khedersolh Sadeh Nasim Khozouei Behrouz Minaei Bidgoli
Semantic role labeling (SRL) is the process of detecting the
predicate-argument structure of each predicate in a sentence. SRL plays a
crucial role as a pre-processing step in many NLP applications such as topic
and concept extraction, question answering, summarization, machine translation,
sentiment analysis, and text mining. Recently, in many languages, unified SRL
dragged lots of attention due to its outstanding performance, which is the
result of overcoming the error propagation problem. However, regarding the
Persian language, all previous works have focused on traditional methods of SRL
leading to a drop in accuracy and imposing expensive feature extraction steps
in terms of financial resources, time and energy consumption. In this work, we
present an end-to-end SRL method that not only eliminates the need for feature
extraction but also outperforms existing methods in facing new samples in
practical situations. The proposed method does not employ any auxiliary
features and shows more than 16 (83.16) percent improvement in accuracy against
previous methods in similar circumstances.