探索神经网络模型中的远距离标记神经元
Exploring Distantly-Labeled Rationales in Neural Network Models
摘要：
近年来的研究试图将人类的各种理论整合到神经网络中以提高模型的性能，但很少有人关注理论的质量。现有的方法大多将模型的关注点完全、均匀地分配给距离较远的理性词，而忽略了潜在的重要非理性词，也没有区分不同理性词的重要性。在本文中，我们提出了两个新的辅助损失函数，以更好地利用远距离标记的理由，这鼓励模型保持对标记理由（PIN）以外的重要词的关注，并减少对无用理由（NoIR）的冗余训练。两个典型的分类任务的实验表明，我们提出的方法可以推动一个分类模型，有效地学习关键线索，从非完美的理由，同时保持其重点扩散到其他未标记的重要词的能力，从而显着优于现有的方法。
英文摘要：
Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existing methods distribute their models' focus to distantly-labeled rationale words entirely and equally, while ignoring the potential important non-rationale words and not distinguishing the importance of different rationale words. In this paper, we propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationales (PINs) and alleviate redundant training on non-helpful rationales (NoIRs). Experiments on two representative classification tasks show that our proposed methods can push a classification model to effectively learn crucial clues from non-perfect rationales while maintaining the ability to spread its focus to other unlabeled important words, thus significantly outperform existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
