Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment.Previous AI research has shown the importance of fundamental and indispensable knowledge of actions, i.e., preconditions and effects.However, traditional methods rely on logical formalization which hinders practical applications.With recent transformer-based language models (LMs), reasoning over text is desirable and seemingly feasible, leading to the question of whether LMs can effectively and efficiently learn to solve RAC problems.We propose four essential RAC tasks as a comprehensive textual benchmark and generate problems in a way that minimizes the influence of other linguistic requirements (e.g., grounding) to focus on RAC.The resulting benchmark, TRAC, encompassing problems of various complexities, facilitates a more granular evaluation of LMs, precisely targeting the structural generalization ability much needed for RAC.Experiments with three high-performing transformers indicate that additional efforts are needed to tackle challenges raised by TRAC.

Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks. While most previous RE methods focus on leveraging deterministic KGs, uncertain KGs, which assign a confidence score for each relation instance, can provide prior probability distributions of relational facts as valuable external knowledge for RE models. This paper proposes to exploit uncertain knowledge to improve relation extraction. Specifically, we introduce ProBase, an uncertain KG that indicates to what extent a target entity belongs to a concept, into our RE architecture. We then design a novel multi-view inference framework to systematically integrate local context and global knowledge across three views: mention-, entity- and concept-view. The experiment results show that our model achieves competitive performances on both sentence- and document-level relation extraction, which verifies the effectiveness of introducing uncertain knowledge and the multi-view inference framework that we design.

The recent success of neural language models (NLMs) on the Winograd Schema Challenge has called for further investigation of the commonsense reasoning ability of these models.Previous diagnostic datasets rely on crowd-sourcing which fails to provide coherent commonsense crucial for solving WSC problems.To better evaluate NLMs, we propose a logic-based framework that focuses on highquality commonsense knowledge.Specifically, we identify and collect formal knowledge formulas verified by theorem provers and translate such formulas into natural language sentences.Based on these true knowledge sentences, adversarial false ones are generated.We propose a new dataset named WINOLOGIC with these sentences.Given a problem in WINOLOGIC, NLMs need to decide whether the plausible knowledge sentences could correctly solve the corresponding WSC problems in a zero-shot setting.We also ask human annotators to validate WINOLOGIC to ensure it is humanagreeable.Experiments show that NLMs still struggle to comprehend commonsense knowledge as humans do, indicating that their reasoning ability could have been overestimated.