Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (2022.acl-long)
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| Challenge: | Massively Multilingual Transformer based Language Models have been shown to be effective on zero-shot transfer across languages, though performance varies from language to language depending on pivot language(s) used for fine-tuning. |
| Approach: | They propose to combine multi-task learning problems with multi-lingual Transformers to model zero-shot transfer across languages. |
| Outcome: | The proposed model can predict zero-shot transfer across languages with a multi-task learning problem with pretraining data in very few languages. |
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
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| Challenge: | Recent advances in training multilingual models on large datasets have shown promising results in knowledge transfer across languages. |
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
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Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Steven Wu, Bryan Wilder
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CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding (2022.findings-acl)
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| Challenge: | Task-oriented personal assistants enable people to interact with devices and services using natural language. |
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