Summary of From N-grams to Pre-trained Multilingual Models For Language Identification, by Thapelo Sindane et al.
From N-grams to Pre-trained Multilingual Models For Language Identification
by Thapelo Sindane, Vukosi Marivate
First submitted to arxiv on: 11 Oct 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 This paper explores the use of N-gram models and Large Pre-trained Multilingual models for Language Identification (LID) across 11 South African languages. The study highlights the importance of data size selection for effective frequency distributions in N-gram models, leading to improved language ranking. For pre-trained multilingual models, the paper compares mBERT, RemBERT, XLM-r, Afri-centric models, and Serengeti, showing that Serengeti outperforms others on average. Additionally, the authors propose a lightweight BERT-based LID model (za_BERT_lid) trained with NHCLT + Vukzenzele corpus, which achieves comparable results to the best-performing Afri-centric models. The study emphasizes the importance of focused-based LID and highlights the advantages of using Serengeti as a superior model for language identification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to identify languages in South Africa. They test different kinds of computer models to see which ones work best. One type is called N-gram models, and another type is called Large Pre-trained Multilingual models. The researchers found that one model, called Serengeti, does a great job of identifying languages. They also came up with their own new model, called za_BERT_lid, which works just as well. This study shows how important it is to use the right approach for language identification and highlights why Serengeti is a good choice. |
Keywords
» Artificial intelligence » Bert » N gram
