Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (D19-1)
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| Challenge: | linguistic typology has shown great promise in pre-neural parsing, but results for neural architectures have been mixed. |
| Approach: | They explore the task of leveraging typology in the context of cross-lingual dependency parsing. |
| Outcome: | The proposed approach improves performance in the context of cross-lingual dependency parsing. |
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Typological Features for Multilingual Delexicalised Dependency Parsing (N19-1)
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| Challenge: | Existing universal models to describe the syntax of languages are debated for decades . a new study examines the plausibility of universal grammars in dependency parsing . |
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| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
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Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models (2026.eacl-long)
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| Challenge: | Existing evidence on the intrinsic difficulty of multilingual modeling is limited to small monolingual models or bilingual models trained from scratch. |
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Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations (2020.emnlp-main)
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| Challenge: | Recent studies consider linguistic typology as a potential source of knowledge to support multilingual natural language processing (NLP) tasks. |
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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. |
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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. |
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Quantifying training challenges of dependency parsers (C18-1)
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| Challenge: | a new metric is introduced to evaluate the difficulty to learn a given class of dependencies . a series of systematic computations using that metric have revealed interesting properties of the 3 considered parsing algorithms . |
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Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
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| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
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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. |
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