Summary of Modeling Orthographic Variation Improves Nlp Performance For Nigerian Pidgin, by Pin-jie Lin et al.
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin
by Pin-Jie Lin, Merel Scholman, Muhammed Saeed, Vera Demberg
First submitted to arxiv on: 28 Apr 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 tackles the challenge of working with Nigerian Pidgin, an oral language with no standardized writing system. The lack of a consistent written form leads to noisy datasets that underperform in natural language processing (NLP) tasks like machine translation and sentiment analysis. To address this issue, the authors develop a phonetic-theoretic framework for word editing, which generates orthographic variations that can be used to augment training data. They demonstrate the effectiveness of this approach by improving performance on both sentiment analysis (+2.1 points) and machine translation (+1.4 BLEU points). This work has implications for NLP models trained on Nigerian Pidgin datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Nigerian Pidgin is a language spoken by 100 million people, but it’s mostly talked about rather than written down. That makes it hard to use computers to understand or generate text in this language. The problem is that there are many different ways to write words and sentences, which makes the data noisy and difficult for machines to learn from. This paper figures out how to create more realistic writing variations to help train models better. They test their approach on two important tasks: translating text from Pidgin to English and understanding the sentiment (positive or negative) of a sentence. The results show that using these new writing variations can improve performance by quite a bit. |
Keywords
* Artificial intelligence * Bleu * Natural language processing * Nlp * Translation
