Automated predictions require explanations to be interpretable by humans. One
type of explanation is a rationale, i.e., a selection of input features such as
relevant text snippets from which the model computes the outcome. However, a
single overall selection does not provide a complete explanation, e.g.,
weighing several aspects for decisions. To this end, we present a novel
self-interpretable model called ConRAT. Inspired by how human explanations for
high-level decisions are often based on key concepts, ConRAT extracts a set of
text snippets as concepts and infers which ones are described in the document.
Then, it explains the outcome with a linear aggregation of concepts. Two
regularizers drive ConRAT to build interpretable concepts. In addition, we
propose two techniques to boost the rationale and predictive performance
further. Experiments on both single- and multi-aspect sentiment classification
tasks show that ConRAT is the first to generate concepts that align with human
rationalization while using only the overall label. Further, it outperforms
state-of-the-art methods trained on each aspect label independently.

提出一种自我解释模型 ConRAT，它从输入文本中提取概念，并使用关键概念的线性聚合来解释结果，以可解释性和预测性能方面超越了最先进的方法。

理念的理性化

Rationalization through Concepts

Automated predictions require explanations to be interpretable by humans.
Past work used attention and rationale mechanisms to find words that predict
the target variable of a document. Often though, they result in a tradeoff
between noisy explanations or a drop in accuracy. Furthermore, rationale
methods cannot capture the multi-faceted nature of justifications for multiple
targets, because of the non-probabilistic nature of the mask. In this paper, we
propose the Multi-Target Masker (MTM) to address these shortcomings. The
novelty lies in the soft multi-dimensional mask that models a relevance
probability distribution over the set of target variables to handle
ambiguities. Additionally, two regularizers guide MTM to induce long,
meaningful explanations. We evaluate MTM on two datasets and show, using
standard metrics and human annotations, that the resulting masks are more
accurate and coherent than those generated by the state-of-the-art methods.
Moreover, MTM is the first to also achieve the highest F1 scores for all the
target variables simultaneously.

本文提出了将 Multi-Target Masker (MTM) 应用于多目标预测的问题，在两个数据集的实验中，MTM 显示出产生更精确、更连贯的预测解释的能力，并且首次同时实现了所有目标变量的最高 F1 分数。