The inference of politically-charged information from text data is a popular
research topic in Natural Language Processing (NLP) at both text- and
author-level. In recent years, studies of this kind have been implemented with
the aid of representations from transformers such as BERT. Despite considerable
success, however, we may ask whether results may be improved even further by
combining transformed-based models with additional knowledge representations.
To shed light on this issue, the present work describes a series of experiments
to compare alternative model configurations for political inference from text
in both English and Portuguese languages. Results suggest that certain text
representations - in particular, the combined use of BERT pre-trained language
models with a syntactic dependency model - may outperform the alternatives
across multiple experimental settings, making a potentially strong case for
further research in the use of heterogeneous text representations in these and
possibly other NLP tasks.

本文利用 transformers（如 BERT）从英语和葡萄牙语文本中实验和比较了不同的模型配置，结果表明对于 “政治推理” 领域，结合 BERT 预训练语言模型和句法依存模型等文本表示法可以在多个实验设置中优化结果，这为进一步研究异构文本表示法在自然语言处理等领域提供了切实的证据。