3 research outputs found
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 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 403
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