Conventional works generally employ a two-phase model in which a generator
selects the most important pieces, followed by a predictor that makes
predictions based on the selected pieces. However, such a two-phase model may
incur the degeneration problem where the predictor overfits to the noise
generated by a not yet well-trained generator and in turn, leads the generator
to converge to a sub-optimal model that tends to select senseless pieces. To
tackle this challenge, we propose Folded Rationalization (FR) that folds the
two phases of the rationale model into one from the perspective of text
semantic extraction. The key idea of FR is to employ a unified encoder between
the generator and predictor, based on which FR can facilitate a better
predictor by access to valuable information blocked by the generator in the
traditional two-phase model and thus bring a better generator. Empirically, we
show that FR improves the F1 score by up to 10.3% as compared to
state-of-the-art methods.

提出了一种 Folded Rationalization 模型，利用文本语义提取将生成器和预测器相融合，改进了传统的两阶段文本理解模型，实现了更好的预测和选择结果，其 F1 得分比现有最新技术提高了 10.3%。