Papers by Iulia Turc
High Performance Natural Language Processing (2020.emnlp-tutorials)
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| Challenge: | a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field . |
| Approach: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
| Outcome: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation (2022.tacl-1)
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| Challenge: | End-to-end neural models have replaced the traditional pipeline and require an explicit tokenization step. |
| Approach: | They propose a neural encoder that operates directly on character sequences without explicit tokenization or vocabulary and a pre-training strategy that optionally uses subwords as a soft inductive bias. |
| Outcome: | The proposed model outperforms a comparable mBert model on a multilingual benchmark by 5.7 F1 on the TyDi QA benchmark. |
Learning Task Sampling Policy for Multitask Learning (2021.findings-emnlp)
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| Challenge: | Existing methods to train multi-task models with auxiliary tasks are limited by the number of combinations and the importance of each auxiliary task is not known a priori. |
| Approach: | They propose a search method that automatically assigns importance weights to auxiliary tasks to improve the target task quality. |
| Outcome: | The proposed method outperforms uniform sampling and the corresponding single-task baseline on XNLI and GLUE. |