Synthetic Propaganda Embeddings To Train A Linear Projection
Adam Ek, Mehdi Ghanimifard · 2019
This paper presents a method of detecting fine-grained categories of propaganda in text.Given a sentence, our method aims to identify a span of words and predict the type of propaganda used.To detect propaganda, we explore a method for extracting features of propaganda from contextualized embeddings without finetuning the large parameters of the base model.We show that by generating synthetic embeddings we can train a linear function with ReLU activation to extract useful labeled embeddings from an embedding space generated by a general-purpose language model.We also introduce an inference technique to detect continuous spans in sequences of propaganda tokens in sentences.A result of the ensemble model is submitted to the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin.In this paper, we provide additional analysis regarding our method of detecting spans of propaganda with synthetically generated representations.