Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks. However, current works on topic segmentation often focus on segmentation of structured texts. In this paper, we comprehensively analyze the generalization capabilities of state-of-the-art topic segmentation models on unstructured texts. We find that: (a) Current strategies of pre-training on a large corpus of structured text such as Wiki-727K do not help in transferability to unstructured texts. (b) Training from scratch with only a relatively small-sized dataset of the target unstructured domain improves the segmentation results by a significant margin.
翻译:根据其语义结构将文件或对话分解成多个毗连部分,这是国家语言方案的一个重要和具有挑战性的问题,可以帮助许多下游任务。然而,目前关于专题分解的工作往往侧重于结构化文本的分解。在本文件中,我们全面分析了非结构化文本方面最先进的专题分解模型的概括能力。我们发现:(a)目前关于诸如Wiki-727K等大量结构化文本的培训前战略无助于向非结构化文本的转换。 (b)从零到零的培训,目标非结构化区域只有相对小的数据集,使分解结果大大改善。