Papers with KCCA
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer (2020.aacl-main)
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| Challenge: | Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. |
| Approach: | They propose to combine embeddings from a pre-trained model with a simple sentence embeddable model. |
| Outcome: | The proposed approach outperforms FT on small datasets with negligible computational overhead. |
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing work on cross-lingual word embeddings rely on linear mappings, but this assumption is not true for all language pairs. |
| Approach: | They propose a non-linear mapping approach which can find non-linesar relationships between languages by kernel Canonical Correlation Analysis. |
| Outcome: | The proposed approach improves on five language pairs on supervised and self-learning scenarios. |