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

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 : ENLS

Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models

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 conversational data. (b) Training from scratch with only a relatively small-sized dataset of the target unstructured domain improves the segmentation results by a significant margin. We stress-test our proposed Topic Segmentation approach by experimenting with multiple loss functions, in order to mitigate effects of imbalance in unstructured conversational datasets. Our empirical evaluation indicates that Focal Loss function is a robust alternative to Cross-Entropy and re-weighted Cross-Entropy loss function when segmenting unstructured and semi-structured chats.Comment: Accepted to IntelliSys 2023. arXiv admin note: substantial text overlap with arXiv:2211.1495

Experimental Evidence of Large Collective Enhancement of Nuclear Level Density and its Significance in Radiative Neutron Capture

The collective enhancement of nuclear level density and its fade out with excitation energy in deformed $^{171}$Yb nucleus has been inferred through an exclusive measurement of neutron spectra.The statistical model analysis of neutron spectra demonstrated a large collective enhancement factor of 40$\pm$3 for the first time, which corroborates with the recent microscopic model predictions but is an anomalous result compared with the measurements in the nearby deformed nuclei. The complete picture of the energy dependent collective enhancement has been obtained by combining with Oslo data below neutron binding energy. The significance of large collective enhancement in radiative neutron capture cross section of astrophysical interest is highlighted.Comment: 12 pages, 5 figure