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Multilingual Coarse Political Stance Classification of Media: Training Detailsby@mediabias
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Multilingual Coarse Political Stance Classification of Media: Training Details

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In this paper, researchers analyze AI-generated news articles' neutrality and stance evolution across languages using authentic news outlet ratings.
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This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

Authors:

(1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus.

F. Training Details

F.1 L/R Classifier

We finetune XLM-RoBERTa large (Conneau et al., 2020) for L vs. R classification as schematised in Figure 1. Our classifier is a small network on top of RoBERTa that first performs dropout with probability 0.1 on RoBERTa’s [CLS] token, followed by a linear layer and a tanh. We pass trough another dropout layer with probability 0.1 and a final linear layer projects into the two classes. The whole architecture is finetuned.


Figure 1: Finetuning architecture.


We use a cross-entropy loss, AdamW optimiser and a learning rate that decreases linearly. We tune the batch size, the learning rate, warmup period and the number of epochs. The best values per language and model are summarised in Table 12.


Table 12: Main hyperparameters used and their performance in the three monolingual finetunings (en, de and, es) and the multilingual one (en+de+es).


All trainings are performed using a single NVIDIA Tesla V100 Volta GPU with 32GB.

F.2 Topic Modelling

We use Mallet (McCallum, 2002) to perform LDA on the corpus after removing the stopwords, with the hyperparameter optimization option activated and done every 10 iterations. Other parameters are the defaults. We do a run per language with 10 topics and another run with 15 topics. We tag the corpus with both labels.