论文标题
达到平衡：减轻对称分类任务的预训练模型的不一致
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks
论文作者
论文摘要
尽管用于下游分类的微调预训练的模型是NLP中的常规范式，但在结果模型中通常不会捕获特定于任务的细微差别。具体而言，对于接受两个输入并要求输出不变的任务，输入顺序不变，通常在预测的标签或置信度得分中观察到不一致。我们强调了这个模型缺点并应用一致性损失函数以减轻对称分类的不一致。我们的结果表明，对于三个释义检测数据集预测的一致性提高了，而准确分数没有显着下降。我们检查了六个数据集（对称和非对称）的分类性能，以展示我们方法的优势和局限性。
While fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models. Specifically, for tasks that take two inputs and require the output to be invariant of the order of the inputs, inconsistency is often observed in the predicted labels or confidence scores. We highlight this model shortcoming and apply a consistency loss function to alleviate inconsistency in symmetric classification. Our results show an improved consistency in predictions for three paraphrase detection datasets without a significant drop in the accuracy scores. We examine the classification performance of six datasets (both symmetric and non-symmetric) to showcase the strengths and limitations of our approach.
