| Challenge: | phylogenetic learning is beneficial for low resourced languages and well furnished languages families. |
| Approach: | They propose to use the phylogenetic tree to guide the learning of multi-lingual dependency parsers . they use a phylogy tree to train models that leverage languages structural similarities . |
| Outcome: | The proposed model outperforms independently learned models on zero-shot parsing of unseen languages. |
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Phylogeny-Inspired Adaptation of Multilingual Models to New Languages (2022.aacl-main)
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| Challenge: | Large pretrained multilingual models have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. |
| Approach: | They propose to use language phylogenetic information to improve cross-lingual transfer by leveraging closely related languages in a structured, linguistically-informed manner. |
| Outcome: | The proposed model significantly improves on the baseline model on languages unseen during training. |
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study (D19-61)
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| Challenge: | Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. |
| Approach: | They compare two approaches to cross-lingual dependency parsing using monolingual source models and a polyglot model which is trained on the combination of all source languages. |
| Outcome: | The proposed methods improve low-resource dependency parsers by transferring syntactic knowledge from one language to another. |
Dependency-based Hybrid Trees for Semantic Parsing (D18-1)
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| Challenge: | Existing models for semantic parsing focus on structure-based models, but none deal with dependency information. |
| Approach: | They propose a dependency-based hybrid tree model which converts natural language utterances into machine interpretable meaning representations. |
| Outcome: | The proposed model achieves state-of-the-art performance across eight languages and is highly tractable inferenceable. |
Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian (L18-1)
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| Challenge: | Developing systems for low-resource languages is a crucial issue for Natural Language Processing (NLP). |
| Approach: | They propose a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corporata of larger languages. |
| Outcome: | The proposed method improves dependency parsing for low-resource languages with very small training corpora compared to previous work . it also explores whether contemporary contact languages or genetically related languages would be the most fruitful starting point for multilingual parsers. |
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. |
Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
| Approach: | They propose a method to make the most of heterogeneous treebanks when training a monolingual parser. |
| Outcome: | The proposed method improves on training with multiple treebanks for a single language. |
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning (2022.acl-short)
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| Challenge: | Large multilingual pretrained language models such as mBERT and XLM-RoBERTa have been found to be effective for cross-lingual transfer of syntactic parsing models but only between related languages. |
| Approach: | They propose to use multi-task learning to dynamically optimize for parsing performance on outlier languages by using a multi-level learning approach. |
| Outcome: | The proposed method significantly outperforms uniform and size-proportional sampling in the zero-shot setting. |
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. |
75 Languages, 1 Model: Parsing Universal Dependencies Universally (D19-1)
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| Challenge: | UDify is a multilingual multi-task model that can predict universal part-of-speech, morphological features, lemmas, and dependency trees. |
| Approach: | They evaluate UDify, a multilingual multi-task model capable of predicting universal part-of-speech, morphological features, lemmas, and dependency trees simultaneously for all 124 Universal Dependencies treebanks across 75 languages. |
| Outcome: | The proposed model can predict universal part-of-speech, morphological features, lemmas, and dependency trees for all 124 treebanks across 75 languages. |
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 . |