论文标题
LIREX：增强语言推论，并用相关解释
LIREx: Augmenting Language Inference with Relevant Explanation
论文作者
论文摘要
自然语言解释（NLE）是一种特殊的数据注释形式，在将标签分配给数据实例时，注释者在其中识别理由（最重要的文本令牌），并根据理由写出对自然语言的标签的解释。 NLE被证明可以更好地捕捉人类推理，但对自然语言推论（NLI）不利。在本文中，我们分析了NLE当前用于训练语言推理任务的解释发生器的两个主要缺陷。我们发现，解释发生器没有考虑到人类对标签的解释固有的可变性，并且当前的解释生成模型会产生虚假的解释。为了克服这些局限性，我们提出了一个新颖的框架Lirex，该框架既包含了启用了理由的解释生成器和实例选择器，又可以选择相关的，合理的NLE来增强NLI模型。当对标准化的SNLI数据集进行评估时，Lirex的精度为91.87％，比基线的0.32提高，并匹配数据集中最佳报告的性能。当转移到室外多网络数据集时，它也比以前的研究要高得多。定性分析表明，Lirex会产生灵活，忠实和相关的NLE，从而使该模型更加强大地解释。该代码可从https://github.com/zhaoxy92/lirex获得。
Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales. NLEs have been shown to capture human reasoning better, but not as beneficial for natural language inference (NLI). In this paper, we analyze two primary flaws in the way NLEs are currently used to train explanation generators for language inference tasks. We find that the explanation generators do not take into account the variability inherent in human explanation of labels, and that the current explanation generation models generate spurious explanations. To overcome these limitations, we propose a novel framework, LIREx, that incorporates both a rationale-enabled explanation generator and an instance selector to select only relevant, plausible NLEs to augment NLI models. When evaluated on the standardized SNLI data set, LIREx achieved an accuracy of 91.87%, an improvement of 0.32 over the baseline and matching the best-reported performance on the data set. It also achieves significantly better performance than previous studies when transferred to the out-of-domain MultiNLI data set. Qualitative analysis shows that LIREx generates flexible, faithful, and relevant NLEs that allow the model to be more robust to spurious explanations. The code is available at https://github.com/zhaoxy92/LIREx.
