Summary of Local Topology Measures Of Contextual Language Model Latent Spaces with Applications to Dialogue Term Extraction, by Benjamin Matthias Ruppik et al.
Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction
by Benjamin Matthias Ruppik, Michael Heck, Carel van Niekerk, Renato Vukovic, Hsien-chin Lin, Shutong Feng, Marcus Zibrowius, Milica Gašić
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach addresses shortcomings in training machine learning classifiers for sequence tagging tasks using contextual word representations. The traditional method considers single input sequences isolatedly and relies on fine-tuning the embedding model with the classifier, which may not be feasible due to the size or inaccessibility of the underlying feature-generation model. To overcome these limitations, the authors introduce complexity measures of the local topology of the latent space of a contextual language model with respect to a given datastore. The effectiveness of these features is demonstrated through their application to dialogue term extraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves machine learning models for sequence tagging tasks by better understanding how words relate to each other. Current methods are limited because they look at individual words separately and rely on fine-tuning, which can be difficult or impossible in some cases. To solve this problem, the authors develop new features that describe how words are connected in a larger space of word meanings. These features help improve dialogue term extraction, a specific task that involves identifying important terms in conversations. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Language model » Latent space » Machine learning
