识别语义困难样本以改进文本分类
Identifying Semantically Difficult Samples to Improve Text Classification
摘要：
在本文中，我们研究了从给定的文本数据集中处理困难样本对下游文本分类任务的影响。我们通过在语义嵌入空间中分析困难样本，将其定义为文本分类的非明显情况;具体而言-（i）属于不同类别的语义相似样本和（ii）属于同一类别的语义不相似样本。我们提出了一个惩罚函数来衡量数据集中每个样本的整体难度得分。我们在13个标准数据集上进行了详尽的实验，以显示高达9%的一致改进，并讨论了定性结果，以显示我们的方法在识别文本分类模型的困难样本方面的有效性。
英文摘要：
In this paper, we investigate the effect of addressing difficult samples from a given text dataset on the downstream text classification task. We define difficult samples as being non-obvious cases for text classification by analysing them in the semantic embedding space; specifically - (i) semantically similar samples that belong to different classes and (ii) semantically dissimilar samples that belong to the same class. We propose a penalty function to measure the overall difficulty score of every sample in the dataset. We conduct exhaustive experiments on 13 standard datasets to show a consistent improvement of up to 9% and discuss qualitative results to show effectiveness of our approach in identifying difficult samples for a text classification model.
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。
