Papers by Telmo Pires
Learning Language-Specific Layers for Multilingual Machine Translation (2023.acl-long)
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| Challenge: | Multilingual Machine Translation (MNMT) is a promising new approach to improve translation quality between non-English languages. |
| Approach: | They propose a language-specific transformer layer to increase model capacity while keeping computation and parameters constant. |
| Outcome: | The proposed approach improves translation quality by 1.3 chrF (1.5 spBLEU) over not using LSLs on a separate decoder architecture. |
Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)
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| Challenge: | Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Approach: | They propose to use a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Outcome: | The proposed models improve translation quality and speed under third-party testing environments. |
How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
| Approach: | They propose a multilingual model pre-trained from monolingual Wikipedia corpora . they show that multilingual BERT is surprisingly good at zero-shot cross-lingual model transfer . |
| Outcome: | The proposed model can find translation pairs, but it exhibits systematic deficiencies affecting certain language pairs. |
End-to-End Speech Translation for Code Switched Speech (2022.findings-acl)
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Orion Weller, Matthias Sperber, Telmo Pires, Hendra Setiawan, Christian Gollan, Dominic Telaar, Matthias Paulik
| Challenge: | Code switching (CS) is the phenomenon of interchangeably using words and phrases from different languages. |
| Approach: | They propose a new ST corpus that extends the joint transcription and translation setup. |
| Outcome: | The proposed model performs well even when no training data is used. |