Compositional Natural Language Inference has been explored to assess the true
abilities of neural models to perform NLI. Yet, current evaluations assume
models to have full access to all primitive inferences in advance, in contrast
to humans that continuously acquire inference knowledge. In this paper, we
introduce the Continual Compositional Generalization in Inference (C2Gen NLI)
challenge, where a model continuously acquires knowledge of constituting
primitive inference tasks as a basis for compositional inferences. We explore
how continual learning affects compositional generalization in NLI, by
designing a continual learning setup for compositional NLI inference tasks. Our
experiments demonstrate that models fail to compositionally generalize in a
continual scenario. To address this problem, we first benchmark various
continual learning algorithms and verify their efficacy. We then further
analyze C2Gen, focusing on how to order primitives and compositional inference
types and examining correlations between subtasks. Our analyses show that by
learning subtasks continuously while observing their dependencies and
increasing degrees of difficulty, continual learning can enhance composition
generalization ability.

本文介绍了 Continual Compositional Generalization in Inference (C2Gen NLI) 挑战，并通过设计一个连续学习设置来探究持续学习对 NLI 的组合推理的影响。实验证明模型在连续场景中无法进行组合推理的泛化。为了解决这个问题，我们首先对各种连续学习算法进行基准测试并验证其有效性，然后进一步分析 C2Gen，关注如何排序原语和组合推理类型，并检查子任务之间的相关性。我们的分析表明，通过在观察其依赖性和难度增加的同时连续学习子任务，可以提高组合推理能力。