Summary of German Text Embedding Clustering Benchmark, by Silvan Wehrli et al.
German Text Embedding Clustering Benchmark
by Silvan Wehrli, Bert Arnrich, Christopher Irrgang
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a benchmark for assessing the performance of clustering German text embeddings in different domains. The increasing use of clustering neural text embeddings in tasks like topic modeling and the need for German resources in existing benchmarks drive this work. An initial analysis is provided, evaluating pre-trained mono- and multilingual models on various clustering algorithms. Results show strong-performing models, with dimensionality reduction further improving clustering performance. Experiments also explore continued pre-training for German BERT models to estimate the benefits of additional training, suggesting significant improvements are possible for short texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a benchmark to test how well text embeddings from Germany do at grouping similar texts together. This is important because people want to use these embeddings in tasks like finding related topics or identifying news stories with similar themes. The authors tested many different models and found that some of them did very well, especially when they reduced the size of the embedding. They also tried training German BERT models even more and saw big improvements for short texts. |
Keywords
» Artificial intelligence » Bert » Clustering » Dimensionality reduction » Embedding
