Abductive natural language inference by interactive model with structural loss

Abstract

The abductive natural language inference task (αNLI) is proposed to infer the most plausible explanation between the cause and the event. In the αNLI task, two observations are given, and the most plausible hypothesis is asked to pick out from the candidates. Existing methods model the relation between each candidate hypothesis separately and penalize the inference network uniformly. In this paper, we argue that it is unnecessary to distinguish the reasoning abilities among correct hypotheses; and similarly, all wrong hypotheses contribute the same when explaining the reasons of the observations. Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called “joint softmax focal loss” in this paper. Based on the observation that the hypotheses are generally semantically related, we design a novel interactive language model aiming at exploiting the rich interaction among competing hypotheses. We name this new model for αNLI: Interactive Model with Structural Loss (IMSL). The experimental results show that our IMSL has achieved the highest performance on the RoBERTa-large pretrained model, with ACC and AUC results increased by about 1% and 5% respectively. We also compared the performance in terms of precision and sensitivity with publicly available code, demonstrating the efficiency and robustness of the proposed approach.