Detecting stance on Twitter is especially challenging because of the short length of each tweet, the continuous coinage of new terminology and hashtags, and the deviation of sentence structure from standard prose. Finetuned language models using large-scale in-domain data have been shown to be the new state-of-the-art for many NLP tasks, including stance detection. In this paper, we propose a novel BERT-based fine-tuning method that enhances the masked language model for stance detection. Instead of random token masking, we propose using a weighted log-odds-ratio to identify words with high stance distinguishability and then model an attention mechanism that focuses on these words. We show that our proposed approach outperforms the state of the art for stance detection on Twitter data about the 2020 US Presidential election.

Knowledge Enhanced Masked Language Model for Stance Detection



<i>**Session Chair: ** Pushkar Mishra </i><br><br>

13E-Oral: Sentiment Analysis and Stylistic Analysis

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Knowledge Enhanced Masked Language Model for Stance Detection

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