Papers by Alexander Jones
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space (2021.emnlp-main)
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| Challenge: | Cross-lingual language models house representations for many different languages in the same space. |
| Approach: | They investigate linguistic and non-linguistic factors affecting sentence-level alignment in cross-lingual pretrained language models for 101 languages and 5,050 language pairs. |
| Outcome: | The results show that word order agreement and agreement in morphological complexity are strongest predictors of cross-linguality. |
Multilingual Models for ASR in Chibchan Languages (2024.naacl-long)
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| Challenge: | Existing algorithms for low resource-intensive languages are not available for these languages . a paper comparing the performance of different models and algorithms for these extremely low resource languages is presented. |
| Approach: | They propose to fine-tune four ASR algorithms to create monolingual models for Bribri and Cabécar . they then use the best performing algorithm to train joint and transfer learning models for both languages . |
| Outcome: | The proposed algorithms are effective in both Bribri and Cabécar, but especially in Bribri. |
Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation (2023.tacl-1)
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| Challenge: | a new architecture learns to use pronunciations of neighboring names to guess pronunciations . features cause not infrequent problems in the US, but become a serious issue in Japan . |
| Approach: | They propose an architecture that learns to use pronunciations of neighboring names to guess pronunciations . they propose corrections for errors in Google Maps and an application to a totally different task . |
| Outcome: | The proposed model can be applied to finding and proposing corrections for errors in Google Maps. |
GATITOS: Using a New Multilingual Lexicon for Low-resource Machine Translation (2023.emnlp-main)
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| Challenge: | a new study explores the effectiveness of bilingual lexica in machine translation models . cross-lingual vocabulary alignment is still highly imperfect in these models, despite the success of supervised and self-supervised training. |
| Approach: | They use a resource to improve translation performance on 200-language models . they show that lexica is more reliable than human-translated data . |
| Outcome: | The proposed approach improves on 200-language translation models with lexical data augmentation . the proposed approach is open-source and has 168 tail languages . |