Unsupervised rationale extraction aims to extract concise and contiguous text snippets to support model predictions without any annotated rationale. Previous studies have used a two-phase framework known as the Rationalizing Neural Prediction (RNP) framework, which follows a generate-then-predict paradigm. They assumed that the extracted explanation, called rationale, should be sufficient to predict the golden label. However, the assumption above deviates from the original definition and is too strict to perform well. Furthermore, these two-phase models suffer from the interlocking problem and spurious correlations. To solve the above problems, we propose a novel single-phase framework called You Only Forward Once (YOFO), derived from a relaxed version of rationale where rationales aim to support model predictions rather than make predictions. In our framework, A pre-trained language model like BERT is deployed to simultaneously perform prediction and rationalization with less impact from interlocking or spurious correlations. Directly choosing the important tokens in an unsupervised manner is intractable. Instead of directly choosing the important tokens, YOFO gradually removes unimportant tokens during forward propagation. Through experiments on the BeerAdvocate and Hotel Review datasets, we demonstrate that our model is able to extract rationales and make predictions more accurately compared to RNP-based models. We observe an improvement of up to 18.4\% in token-level F1 compared to previous state-of-the-art methods. We also conducted analyses and experiments to explore the extracted rationales and token decay strategies. The results show that YOFO can extract precise and important rationales while removing unimportant tokens in the middle part of the model.

无监督的理由提取旨在提取简洁而连续的文本片段以支持模型预测，而无需任何已注释的理由。通过使用一种名为“ You Only Forward Once (YOFO)”的新型单阶段框架，本研究在BeerAdvocate和Hotel Review数据集上进行实验证明我们的模型在提取理由和进行预测方面比基于RNP的模型更准确，与之前最先进的方法相比，在标记级F1方面提高了高达18.4％。同时，我们还对提取的理由和标记衰减策略进行了分析和实验证明YOFO可以提取精确而重要的理由，同时删除模型中间部分的不重要的标记。

只需一次前向传递：在单个前向传递中的预测和合理化