Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats

Autor:	Ghosh, Reshmi, Kajal, Harjeet Singh, Kamath, Sharanya, Shrivastava, Dhuri, Basu, Samyadeep, Srinivasan, Soundararajan
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: NeurIPS 2022 : ENLSP
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.14954 Zobrazit plný text záznamu View this record from Arxiv