Summary of Transcribing Bengali Text with Regional Dialects to Ipa Using District Guided Tokens, by S M Jishanul Islam et al.
Transcribing Bengali Text with Regional Dialects to IPA using District Guided Tokens
by S M Jishanul Islam, Sadia Ahmmed, Sahid Hossain Mustakim
First submitted to arxiv on: 26 Mar 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 This paper presents a novel approach to transcribing Bengali text into International Phonetic Alphabet (IPA) format, which is crucial for languages with complex phonology. The authors tackle this challenge by introducing District Guided Tokens (DGT), a technique that provides explicit information about the regional dialect of the input text before generating the IPA transcription. This is achieved by prepending a district token to the input sequence, guiding the model to understand unique phonetic patterns associated with each district. The DGT technique is applied to fine-tune transformer-based models on a new dataset spanning six districts of Bangladesh. Experimental results demonstrate the effectiveness of DGT, with ByT5 achieving superior performance over word-based models like mT5, BanglaT5, and umT5. This highlights the importance of incorporating regional dialect information into natural language processing systems for languages with diverse phonological variations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand Bengali text better by creating a way to turn it into IPA format. It’s hard because Bengali has many different sounds that change depending on where you are in Bangladesh. The authors came up with an idea called District Guided Tokens, which is like giving the computer a hint about where the text is from before it tries to convert it. This helps the computer do a better job of converting the text into IPA format. They tested this idea using special models and showed that it works really well for one type of model called ByT5. This is important because it can help us make computers that understand languages better. |
Keywords
* Artificial intelligence * Natural language processing * Token * Transformer
