Summary of Mitigating the Linguistic Gap with Phonemic Representations For Robust Cross-lingual Transfer, by Haeji Jung et al.
Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer
by Haeji Jung, Changdae Oh, Jooeon Kang, Jimin Sohn, Kyungwoo Song, Jinkyu Kim, David R. Mortensen
First submitted to arxiv on: 22 Feb 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 In this study, researchers investigate how different input-level representations affect performance gaps in multilingual language understanding. They focus on phonemic inputs, which represent sounds rather than written words, to mitigate these gaps. The team presents experiments on three representative cross-lingual tasks across 12 languages, showing that phonemic representations exhibit higher similarities between languages compared to orthographic representations. Phonemic-based models consistently outperform grapheme-based baseline models on low-resource languages, providing quantitative evidence and theoretical justification for the effectiveness of phonemic representations in improving multilingual language understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Phonemic representations are a way to improve how computers understand different languages. Right now, some languages have more computer training data than others, which makes it harder for computers to understand these “low-resource” languages. The researchers thought that if they used sounds instead of written words to represent languages, this might help bridge the gap between high- and low-resource languages. They tested their idea on three tasks across 12 languages and found that using phonemic representations helped computers perform better on low-resource languages. This is important because it could help computers communicate more effectively with people who speak these languages. |
Keywords
» Artificial intelligence » Language understanding
