Papers by Richard Tzong-Han Tsai
Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems (2024.lrec-main)
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| Challenge: | Currently, machine translation systems cater to high-resource languages (HRLs), while low-resourced languages (LRLs) like Taiwanese Hokkien are relatively under-explored. |
| Approach: | They propose to use a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin China. |
| Outcome: | The proposed model bridges the gap between Taiwanese Hokkien and other low-resource languages by using a pre-trained LLaMA 2-7B model and a monolingual corpus. |
Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding (N18-2)
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| Challenge: | Existing methods to create interlanguage links between encyclopedias are time-consuming and difficult. |
| Approach: | They propose a method to find corresponding article pairs of different languages in encyclopedias by cross-encyclopae entity embedding. |
| Outcome: | The proposed method improves performance over baseline by 29.62% compared to the current best system. |
Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien (2022.findings-emnlp)
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| Challenge: | CM is a challenging task when mixed languages include dialects. |
| Approach: | They propose to construct a Hokkien-Mandarin CM dataset to overcome the limitation . they propose to use a linguistics-based toolkit to train the model for translation tasks . |
| Outcome: | The proposed model achieves good results on CM data translation while maintaining monolingual translation quality. |
BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset (2022.coling-1)
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| Challenge: | Code-mixing is prevalent in multilingual societies and is challenging to train . we use data augmentation to build a model to deal with code-mixed inputs . |
| Approach: | They propose to train a model to deal with code-mixing phenomena of Bahasa Rojak using data augmentation to construct a Bahasan Rojakin corpus and a pre-trained model to process input tokens. |
| Outcome: | The proposed model can tag the language of the input token automatically to process code-mixing input. |