论文标题
FR：用统一编码器折叠合理化
FR: Folded Rationalization with a Unified Encoder
论文作者
论文摘要
常规作品通常采用两阶段模型，其中发电机选择最重要的部分，然后是根据所选零件进行预测的预测因子。但是，这样的两相模型可能会引起变性问题，在这种问题中，预测变量过度地适合尚未训练的发电机生成的噪声，然后导致发电机收敛到倾向于选择无意义的零件的亚最佳模型。为了应对这一挑战，我们提出了折叠的合理化（FR），将理由模型的两个阶段折叠成一个文本语义提取的角度。 FR的关键思想是在发电机和预测变量之间采用统一的编码器，基于FR可以通过访问传统两相模型中发电机阻止的有价值的信息来促进更好的预测指标，从而带来更好的生成器。从经验上讲，我们表明，与最新方法相比，FR将F1得分提高了10.3％。
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.
