Papers with WALS
Multilingual Gradient Word-Order Typology from Universal Dependencies (2024.eacl-short)
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| Challenge: | Existing typological databases, including WALS and Grambank, suffer from inconsistencies due to categorical format. |
| Approach: | They propose a new seed dataset that uses continuous-valued data instead of categorical data to better reflect the variability of language. |
| Outcome: | The proposed dataset can be easily adapted to generate data for a broader set of features and languages. |
Does Typological Blinding Impede Cross-Lingual Sharing? (2021.eacl-main)
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| Challenge: | Existing work on bridging the performance gap between high- and low-resource languages has only found minor benefits from using typological information. |
| Approach: | They propose to use typological features to train models in a cross-lingual setting to learn latent weights between languages. |
| Outcome: | The proposed model overshadows the utility of explicitly using typological features by ignoring them, and shows that encouraging sharing according to typology improves performance. |
The Past, Present, and Future of Typological Databases in NLP (2023.findings-emnlp)
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| Challenge: | Typological information is inconsistent with each other and other sources of typological information, such as linguistic grammars. |
| Approach: | They propose to examine disagreements between typological databases and their uses in NLP by exploring disagreements across databases and resources. |
| Outcome: | The proposed view of typology has significant potential in the future, including in language modeling in low-resource scenarios. |
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings (N18-1)
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| Challenge: | linguistic typology is the classification of languages according to their linguistic properties. |
| Approach: | They learn distributed language representations which can be used to predict typological properties on a massively multilingual scale. |
| Outcome: | The proposed model can predict typological properties on a massively multilingual scale. |
Language Embeddings for Typology and Cross-lingual Transfer Learning (2021.acl-long)
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
| Outcome: | The proposed model can be leveraged in cross-lingual tasks without parallel data . the proposed model is based on the World Atlas of Language Structures (WALS) and two extrinsic tasks . |