Topic segmentation is critical for obtaining structured long documents and
improving downstream tasks like information retrieval. Due to its ability of
automatically exploring clues of topic shift from a large amount of labeled
data, recent supervised neural models have greatly promoted the development of
long document topic segmentation, but leaving the deeper relationship of
semantic coherence and topic segmentation underexplored. Therefore, this paper
enhances the supervised model's ability to capture coherence from both
structure and similarity perspectives to further improve the topic segmentation
performance, including the Topic-aware Sentence Structure Prediction (TSSP) and
Contrastive Semantic Similarity Learning (CSSL). Specifically, the TSSP task is
proposed to force the model to comprehend structural information by learning
the original relations of adjacent sentences in a disarrayed document, which is
constructed by jointly disrupting the original document at the topic and
sentence levels. In addition, we utilize inter- and intra-topic information to
construct contrastive samples and design the CSSL objective to ensure that the
sentences representations in the same topic have higher semantic similarity,
while those in different topics are less similar. Extensive experiments show
that the Longformer with our approach significantly outperforms old
state-of-the-art (SOTA) methods. Our approach improves $F_{1}$ of old SOTA by
3.42 (73.74 -> 77.16) and reduces $P_{k}$ by 1.11 points (15.0 -> 13.89) on
WIKI-727K and achieves an average reduction of 0.83 points on $P_{k}$ on
WikiSection. The average $P_{k}$ drop of 2.82 points on the two out-of-domain
datasets also illustrates the robustness of our approach

通过对主题感知句子结构预测（TSSP）和对比语义相似度学习（CSSL）的改进，本文提升了监督模型从结构和相似性两个角度捕捉一致性的能力，进而进一步改善主题划分性能。

通过增强一致性建模改进长文档主题分割模型

Improving Long Document Topic Segmentation Models With Enhanced  Coherence Modeling

Classification of movement trajectories has many applications in
transportation. Supervised neural models represent the current
state-of-the-art. Recent security applications require this task to be rapidly
employed in environments that may differ from the data used to train such
models for which there is little training data. We provide a neuro-symbolic
rule-based framework to conduct error correction and detection of these models
to support eventual deployment in security applications. We provide a suite of
experiments on several recent and state-of-the-art models and show an accuracy
improvement of 1.7% over the SOTA model in the case where all classes are
present in training and when 40% of classes are omitted from training, we
obtain a 5.2% improvement (zero-shot) and 23.9% (few-shot) improvement over the
SOTA model without resorting to retraining of the base model.

利用基于规则的神经符号框架，改进了对运动轨迹进行分类的任务，用于支持安全应用部署，实验证明与目前最先进模型相比，其在训练时所有类别都出现的情况下准确率提高了 1.7％，在 40％的类别被省略的情况下，零样本和几样本情况下其准确率分别提高了 5.2％和 23.9％，而无需重新训练基础模型。