Temporal Natural Language Inference: Evidence-Based Evaluation of Temporal Text Validity.

It is important to learn whether text information remains valid or not for various applications including story comprehension, information retrieval, and user state tracking on microblogs and via chatbot conversations. It is also beneficial to deeply understand the story. However, this kind of inference is still difficult for computers as it requires temporal commonsense. We propose a novel task, Temporal Natural Language Inference, inspired by traditional natural language reasoning to determine the temporal validity of text content. The task requires inference and judgment whether an action expressed in a sentence is still ongoing or rather completed, hence, whether the sentence still remains valid, given its supplementary content. We first construct our own dataset for this task and train several machine learning models. Then we propose an effective method for learning information from an external knowledge base that gives hints on temporal commonsense knowledge. Using prepared dataset, we introduce a new machine learning model that incorporates the information from the knowledge base and demonstrate that our model outperforms state-of-the-art approaches in the proposed task.