Papers by Silvia Terragni
Cross-lingual Contextualized Topic Models with Zero-shot Learning (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing topic models are language-specific and cannot be transferred in a transferable manner. |
| Approach: | They propose a zero-shot cross-lingual topic model that learns topics on one language and predicts them for unseen documents in different languages. |
| Outcome: | The proposed model learns topics on one language and predicts them for unseen documents in different languages. |
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2021.acl-short)
Copied to clipboard
| Challenge: | Recent neural topic models extract words from documents, but they are not coherent . coherence is crucial for topic models, but many use bag-of-words document representations as input . pre-trained language models are becoming ubiquitous in natural language processing . |
| Approach: | They combine contextualized representations with neural topic models to produce more coherent topics . they say that future improvements in language models will translate into better topic models . |
| Outcome: | The proposed approach produces more meaningful and coherent topics than bag-of-words models and recent neural models. |
OCTIS: Comparing and Optimizing Topic models is Simple! (2021.eacl-demos)
Copied to clipboard
| Challenge: | Current topic modeling frameworks focus on preprocessing, evaluation, comparison of models and visualization. |
| Approach: | They propose an evaluation framework for Topic Models with optimal hyper-parameters estimated using Bayesian Optimization approach. |
| Outcome: | The proposed framework integrates several state-of-the-art topic models and evaluation metrics. |