Positional Transformers for Claim Span Identification

Given the vast amount of misinformation present in today’s social media environment, it is critical to be able to identify claims made in social media posts to facilitate the fact-verification process. For this reason, the CLAIMSCAN (Task B) shared task introduces the objective of claim span identification, which requires identifying spans of text within tweets that correspond to (allegedly) factual claims made by users. In this submission to CLAIMSCAN Task B, we introduce the positional transformer architecture for claim span identification. This architecture utilizes a novel, position-sensitive attention mechanism that is able to outperform all other submissions to the shared task, but still falls behind a few of the task organizers’ more complex baseline models. In this paper, we discuss the positional transformer architecture, the training and data pre-processing procedures used for CLAIMSCAN Task B, and our results on this task.