Papers with WikiAnn
Toward More Meaningful Resources for Lower-resourced Languages (2022.findings-acl)
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| Challenge: | a new position paper examines how meaningful resources for lower-resourced languages should be developed in connection with the speakers of those languages. |
| Approach: | They propose a position paper on how meaningful resources should be developed for lower-resourced languages . they examine the contents of Wikidata for a few lower-rsourced languages and examine quality issues . |
| Outcome: | The proposed approach is based on the findings of a recent study on the use of multilingual resources in language technology development. |
Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
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| Challenge: | Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks. |
| Approach: | They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations. |
| Outcome: | The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings. |
XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models (2023.emnlp-main)
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Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa
| Challenge: | Large multilingual models rely on a single vocabulary shared across 100+ languages . this vocabulary bottleneck limits the representational capabilities of multilingual model XLM-R . |
| Approach: | They propose a new approach for scaling to large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. |
| Outcome: | The proposed model outperforms XLM-R on all language tasks and is particularly effective on low-resource tasks. |