To address the challenge of interpreting evolving themes in temporal text, we present DTECT (Dynamic Topic Explorer & Context Tracker), an interactive, end-to-end system for uncovering thematic dynamics. The system integrates a complete pipeline that supports data preprocessing, multiple model architectures, and dedicated metrics to analyze temporal topic quality. To enhance interpretability, DTECT features LLM-driven automatic topic labeling, trend analysis, interactive visualizations with document summarization, and a natural language chat interface. This cohesive platform empowers users to intuitively explore how topics change over time.

Modeling latent representations in a hyperspherical space has proven effective for capturing directional similarities in high-dimensional text data, benefiting topic modeling.Variational autoencoder-based neural topic models (VAE-NTMs) commonly adopt the von Mises-Fisher prior to encode hyperspherical structure.However, VAE-NTMs often suffer from posterior collapse, where the KL divergence term in the objective function highly diminishes, leading to ineffective latent representations.To mitigate this issue while modeling hyperspherical structure in the latent space, we propose the Spherical Sliced Wasserstein Autoencoder for Topic Modeling (S2WTM).S2WTM employs a prior distribution supported on the unit hypersphere and leverages the Spherical Sliced-Wasserstein distance to align the aggregated posterior distribution with the prior.Experimental results demonstrate that S2WTM outperforms state-of-the-art topic models, generating more coherent and diverse topics while improving performance on downstream tasks. github.com/AdhyaSuman/S2WTM

Suman Adhya, Debarshi Kumar Sanyal. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset.Although the effectiveness of this regularization trick has been extensively studied for convolutional neural networks, there is a lack of analysis of it for unsupervised models and in particular, VAE-based neural topic models.In this paper, we have analyzed the consequences of dropout in the encoder as well as in the decoder of the VAE architecture in three widely used neural topic models, namely, contextualized topic model (CTM), ProdLDA, and embedded topic model (ETM) using four publicly available datasets.We characterize the dropout effect on these models in terms of the quality and predictive performance of the generated topics.