A Joint Learning Approach for Semi-supervised Neural Topic Modeling

• URL: http://arxiv.org/abs/2204.03208v1
• Date: Thu, 7 Apr 2022 04:42:17 GMT
• Title: A Joint Learning Approach for Semi-supervised Neural Topic Modeling
• Authors: Jeffrey Chiu, Rajat Mittal, Neehal Tumma, Abhishek Sharma, Finale Doshi-Velez
• Abstract summary: We introduce the Label-Indexed Neural Topic Model (LI-NTM), which is the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks.
• Score: 25.104653662416023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.

Related papers

• Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings [10.095706051685665]
  We compare topic models built using traditional statistical models (LDA and NMF) and the BERT-based model. We find that while quantitative metrics prefer statistical models, qualitative evaluation finds better insights from the neural model.
  arXiv  Detail & Related papers  (2024-06-27T05:38:49Z)
• Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis [5.757610495733924]
  We conduct the first evaluation of neural, supervised and classical topic models in an interactive task based setting. We show that current automated metrics do not provide a complete picture of topic modeling capabilities.
  arXiv  Detail & Related papers  (2024-01-29T17:54:04Z)
• Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling [0.6445605125467573]
  Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset. 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.
  arXiv  Detail & Related papers  (2023-03-28T13:45:39Z)
• Are Neural Topic Models Broken? [81.15470302729638]
  We study the relationship between automated and human evaluation of topic models. We find that neural topic models fare worse in both respects compared to an established classical method.
  arXiv  Detail & Related papers  (2022-10-28T14:38:50Z)
• Neural Topic Modeling with Deep Mutual Information Estimation [23.474848535821994]
  We propose a neural topic model which incorporates deep mutual information estimation. NTM-DMIE is a neural network method for topic learning. We evaluate NTM-DMIE on several metrics, including accuracy of text clustering, with topic representation, topic uniqueness and topic coherence.
  arXiv  Detail & Related papers  (2022-03-12T01:08:10Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Have you tried Neural Topic Models? Comparative Analysis of Neural and Non-Neural Topic Models with Application to COVID-19 Twitter Data [11.199249808462458]
  We conduct a comparative study examining state-of-the-art neural versus non-neural topic models. We show that neural topic models outperform their classical counterparts on standard evaluation metrics. We also propose a novel regularization term for neural topic models, which is designed to address the well-documented problem of mode collapse.
  arXiv  Detail & Related papers  (2021-05-21T07:24:09Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Improving Neural Topic Models using Knowledge Distillation [84.66983329587073]
  We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality.
  arXiv  Detail & Related papers  (2020-10-05T22:49:16Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.