| Challenge: | Existing methods for inference of parameter parameters are time-consuming and difficult to use. |
| Approach: | They propose two efficient neural mixed counting models that use the negative binomial distribution as the prior for dispersed topic discovery. |
| Outcome: | The proposed models outperform state-of-the-art models in terms of perplexity and topic coherence on real-world datasets. |
Similar Papers
TAN-NTM: Topic Attention Networks for Neural Topic Modeling (2021.acl-long)
Copied to clipboard
| Challenge: | Topic models have been widely used to learn text representations and gain insight into document corpora. |
| Approach: | They propose a framework which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. |
| Outcome: | The proposed model improves on two downstream tasks: document classification and topic guided keyphrase generation. |
Topic Balancing with Additive Regularization of Topic Models (2020.acl-srw)
Copied to clipboard
| Challenge: | Existing methods for topic modelling on unbalanced data contain topics in various proportions and documents of the relatively small theme become distributed all over the larger topics instead of being grouped into one topic. |
| Approach: | They propose a new regularizer for topic models on unbalanced data collections . they make sure this regularizer increases the quality of topic models, trained on unstructured data . |
| Outcome: | The proposed method improves the quality of topic models trained on unbalanced datasets. |
Tree-Structured Neural Topic Model (2020.acl-main)
Copied to clipboard
| Challenge: | Existing topic models do not organize topics into coherent groups or hierarchies. |
| Approach: | They propose a tree-structured neural topic model with an infinite number of branches and a topic distribution over a forest. |
| Outcome: | The proposed model improves data scalability and competitive performance when inducing latent topics and tree structures. |
Neural Models for Documents with Metadata (P18-1)
Copied to clipboard
| Challenge: | specialized models are often used to model text corpora without metadata . specialized algorithms are not widely used in the digital humanities and political science fields . |
| Approach: | They propose a general neural framework based on topic models to enable customization of metadata. |
| Outcome: | The proposed framework achieves strong performance with a manageable tradeoff between perplexity, coherence, and sparsity. |
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for topic modeling learn topics with a flat structure . however, such methods have data scalability issues . |
| Approach: | They propose to use nonparametric neural variational inference to extract a tree-structured topic model with reasonable structure, low redundancy, and adaptable widths. |
| Outcome: | The proposed model extracts a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. |
Benchmarking Neural Topic Models: An Empirical Study (2021.findings-acl)
Copied to clipboard
| Challenge: | Neural topic modeling has been attracting much attention recently due to its ability to leverage the advantages of both neural networks and probabilistic topic models. |
| Approach: | They propose to evaluate neural topic models in three tasks using large datasets and a set of metrics to compare them. |
| Outcome: | The proposed models perform better in the first and third tasks than the traditional probabilistic models and are better in many cases. |
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder (2020.emnlp-main)
Copied to clipboard
| Challenge: | Topic models for short texts suffer from data sparsity because of limited word co-occurrences. |
| Approach: | They propose a neural topic model with a new topic distribution quantization approach that generates peakier distributions that are more appropriate for modeling short texts. |
| Outcome: | The proposed model outperforms both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics. |
Nonparametric Forest-Structured Neural Topic Modeling (2022.coling-1)
Copied to clipboard
| Challenge: | Existing hierarchical neural topic models can only extract topics at the same level. |
| Approach: | They propose to use self-attention mechanism to capture parent-child topic relationships and build a sparse directed acyclic graph to form a topic forest. |
| Outcome: | The proposed model outperforms baseline models on topic hierarchical rationality and affinity. |
Towards Reinterpreting Neural Topic Models via Composite Activations (2022.emnlp-main)
Copied to clipboard
| Challenge: | Most Neural Topic Models (NTMs) use a variational auto-encoder framework producing K topics limited to the size of the encoder’s output. |
| Approach: | They propose a model-free two-stage process to reinterpret NTM and derive further insights on the state of the trained model. |
| Outcome: | The proposed model-free process decouples the strict interpretation of topics from the original NTM and evaluates them on a large external corpus. |
Neural Attention-Aware Hierarchical Topic Model (2021.emnlp-main)
Copied to clipboard
| Challenge: | Neural topic models (NTMs) use deep neural networks to learn topic information. |
| Approach: | They propose a variational autoencoder model that reconstructs sentence and document word counts using bag-of-words embeddings and pre-trained semantic embedders. |
| Outcome: | The proposed model lowers reconstruction errors at sentence and document levels and finds more coherent topics from real-world datasets. |