Incomplete Observations Bias Suppression for Abductive Natural Language Inference

Abstract

Abductive natural language commonsense reasoning is a task aiming at inferring the most plausible explanation in narrative text for observed events. Previous works mostly concentrate on utilizing powerful pre-trained language models and making better use of excess training data to learn abundant event commonsense knowledge. However, the utilization of causal effect is hidden in the language reasoning process and the explicit constraint of the causal effect between events has not been explored, resulting in biased inference. The model may focus on one observed event and make the wrong prediction while ignoring the other helpful events. To reveal the problem we modify the original task by appending unrelated text to the context which won’t change the causal relation. And typical methods get worse in the new task as they are not good at utilizing the complementary between the two observations. Motivated by eliminating the shortcut from incomplete observation and utilizing the complementarity of the two observations, we propose an incomplete observation bias suppression method to guide the training process. Results show our approach can ease the problem revealed in the new task. Based on the proposed method and the new task, our method also get competitive result on the original task.