Papers with multilingual dependency parsing

5 papers
UDapter: Language Adaptation for Truly Universal Dependency Parsing (2020.emnlp-main)

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Challenge: Cross-language interference and restrained model capacity remain major obstacles in multilingual dependency parsing.
Approach: They propose a multilingual task adaptation approach based on contextual parameter generation and adapter modules that learn adapters via language embeddings while sharing model parameters across languages.
Outcome: The proposed approach outperforms strong monolingual and multilingual baselines on most languages on high-resource and low-resourced (zero-shot) languages.
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs (N19-1)

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Challenge: Recent research has found that a shared bilingual word embedding space can be induced by projecting monolingual word embeds from two languages without any bilingual supervision.
Approach: They propose a framework for learning unsupervised multilingual word embeddings that mitigates instability issues for distant language pairs.
Outcome: The proposed framework outperforms the state-of-the-art methods on two downstream tasks outperforming even supervised baselines.
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)

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Challenge: Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages.
Approach: They propose to solve two sub-problems together to learn a shared embedding space for several languages.
Outcome: The proposed approach outperforms existing methods in bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing tasks.
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 .
Approach: They propose to use typological features to describe the syntax of languages to train a multilingual dependency parser.
Outcome: The proposed model can be trained on 40 languages with the help of typological features.
Climbing the Tower of Treebanks: Improving Low-Resource Dependency Parsing via Hierarchical Source Selection (2021.findings-acl)

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Challenge: Recent work on multilingual dependency parsing focused on developing highly multilingual parsers . a recent major paradigm shift in NLP towards largescale pretrained language models has reduced the downstream relevance of supervised syntactic parse.
Approach: They propose a heuristic approach to multilingual dependency parsing that heurs out the "one model to rule them all" approach by hierarchically clustering all Universal Dependencies languages based on their syntactic similarity .
Outcome: The proposed approach outperforms a "one model to rule them all" approach with a heuristic selection of languages and treebanks for a target language.

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