SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the
contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.
The explosion of online health news articles runs the risk of the proliferation of low-quality information. Within the existing work on fact-checking, however, relatively little attention has been paid to medical news. We present a health news classi
fication task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists. We present a dataset of 1,119 health news paired with systematic reviews. The review criteria consist of six elements that are essential to the accuracy of medical news. We then present experiments comparing the classical token-based approach with the more recent transformer-based models. Our results show that detecting qualitative lapses is a challenging task with direct ramifications in misinformation, but is an important direction to pursue beyond assigning True or False labels to short claims.
