Papers by Noëmi Aepli
Improving Zero-Shot Cross-lingual Transfer Between Closely Related Languages by Injecting Character-Level Noise (2022.findings-acl)
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| Challenge: | Existing approaches to improve cross-lingual transfer do not take surface similarity into account. |
| Approach: | They propose to augment source language training data with character-level noise to simulate spelling variations. |
| Outcome: | The proposed strategy shows consistent improvements over several languages and tasks. |
On Biasing Transformer Attention Towards Monotonicity (2021.naacl-main)
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| Challenge: | Existing work has focused on learning monotonic attention behavior via specialized attention functions or pretraining. |
| Approach: | They introduce a monotonicity loss function compatible with standard attention mechanisms and test it on sequence-to-sequence tasks. |
| Outcome: | The proposed monotonicity loss function can achieve largely monotonic behavior on grapheme-to-phoneme conversion, morphological inflection, transliteration, and dialect normalization tasks. |
A Tulu Resource for Machine Translation (2024.lrec-main)
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| Challenge: | Using parallel datasets, we train a machine translation system in English–Tulu . |
| Approach: | They present a parallel dataset for English–Tulu translation using human translations into the multilingual machine translation resource FLORES-200. |
| Outcome: | The proposed model outperforms Google Translate by 19 BLEU points (in September 2023). |