Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task

2019-10-31 · LREC 2020 5 · Valeriya Slovikovskaya

In this paper, we report improved results of the Fake News Challenge Stage 1 (FNC-1) stance detection task. This gain in performance is due to the generalization power of large language models based on Transformer architecture, invented, trained and publicly released over the last two years. Specifically (1) we improved the FNC-1 best performing model adding BERT sentence embedding of input sequences as a model feature, (2) we fine-tuned BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained state-of-the-art results on FNC-1 task.

📄 PDF Abstract BibTeX arXiv:1910.14353

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Tasks

SentenceSentence EmbeddingSentence-EmbeddingStance Detection Transfer Learning

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