Open information extraction is an important NLP task that targets extracting structured information from unstructured text without limitations on the relation type or the domain of the text.
While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.
Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions.
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension.
In this paper, we present language model system submitted to SemEval-2020 Task 4 competition: "Commonsense Validation and Explanation".
We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame.