Summary of On the Applicability Of Zero-shot Cross-lingual Transfer Learning For Sentiment Classification in Distant Language Pairs, by Andre Rusli et al.
On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for Sentiment Classification in Distant Language Pairs
by Andre Rusli, Makoto Shishido
First submitted to arxiv on: 24 Dec 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 The research investigates the effectiveness of cross-lingual transfer learning from English to Japanese and Indonesian using the XLM-R pre-trained model. The study compares its results with previous works that employed similar zero-shot or fully-supervised approaches to evaluate the capabilities of XLM-R in zero-shot transfer learning. The models achieve state-of-the-art performance on one Japanese dataset and comparable results on other datasets in Japanese and Indonesian languages without training on the target language. Furthermore, the findings suggest that a multi-lingual model can be trained instead of individual models for each language, leading to promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how well a machine learning model called XLM-R works when it’s used to learn from English text and then applied to Japanese and Indonesian languages without being specifically trained on those languages. The researchers compare their results with previous studies that did something similar. They found that their approach was the best for one Japanese dataset, and close to the best for others in both Japanese and Indonesian. This shows that it’s possible to train a single model that can be used for many languages, rather than training separate models for each language. |
Keywords
» Artificial intelligence » Machine learning » Supervised » Transfer learning » Zero shot
