Papers with CLTL
Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation (2021.findings-acl)
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| Challenge: | a new CLTL model is proposed to facilitate cross-linguistic transfer learning between distant languages . a key to CLTL is to learn a shared representation space for the given source-target language pair. |
| Approach: | They propose a new CLTL model that integrates machine translation with MT . they use an unannotated data technique to make use of the model's pre-training and fine-tuning . |
| Outcome: | The proposed model achieves better CLTL performance than the baseline model without more annotated data. |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics (2023.findings-emnlp)
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| Challenge: | Existing datasets for event detection (ED) are limited to a small set of popular languages due to the high cost of data annotation. |
| Approach: | They propose a method to develop cross-lingual transfer learning models in high-resource source languages . they aim to transition the representations for target-language examples into the source-language space . |
| Outcome: | Extensive experiments over three languages demonstrate the state-of-the-art in CLTL. |