On the Relation between Syntactic Divergence and Zero-Shot Performance
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Abstract (arXiv)
We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges-a gap we aim to address. As a test case, we take the transfer of Universal Dependencies (UD) parsing from English to a diverse set of languages and conduct two sets of experiments. In one, we analyze zero-shot performance based on the extent to which English source edges are preserved in translation. In another, we apply three linguistically motivated transformations to UD, creating more cross-lingually stable versions of it, and assess their zero-shot parsability. In order to compare parsing performance across different schemes, we perform extrinsic evaluation on the downstream task of cross-lingual relation extraction (RE) using a subset of a popular English RE benchmark translated to Russian and Korean. In both sets of experiments, our results suggest a strong relation between cross-lingual stability and zero-shot parsing performance.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 9 Oct 2021.
Builds on
20 of 38 references.
References · 20
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
- 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
- 2019Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsingcited by 69
- 2021Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processingcited by 68
- 2019Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsingcited by 46
- 2016Many Languages, One Parsercited by 26
- 2019Enhancing Machine Translation with Dependency-Aware Self-Attentioncited by 24
- 2017Universal Semantic Parsingcited by 21
- 2017Improved Neural Machine Translation with a Syntax-Aware Encoder and Decodercited by 16
- 2019Polyglot Contextual Representations Improve Crosslingual Transfercited by 16
- 2019Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsingcited by 11
8 more
- 2017Syntax-Directed Attention for Neural Machine Translationcited by 10
- 2018Multitask Parsing Across Semantic Representationscited by 10
- 2020Fine-Grained Analysis of Cross-Linguistic Syntactic Divergencescited by 9
- 2019Low-Resource Syntactic Transfer with Unsupervised Source Reorderingcited by 8
- 2020Lexical Semantic Recognitioncited by 8
- 2020pyBART: Evidence-based Syntactic Transformations for IEcited by 5
- 2021Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformationcited by 5
- 2021Part of Speech and Universal Dependency effects on English Arabic Machine Translationcited by 1
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1.
4 papers.
- 2023MultiTACRED: A Multilingual Version of the TAC Relation Extraction Datasetcited by 5
- 2023Improving Cross-Lingual Transfer through Subtree-Aware Word Reorderingcited by 4
- 2022Word-order typology in Multilingual BERT: A case study in subordinate-clause detectioncited by 3
- 2024Assessing the Role of Lexical Semantics in Cross-lingual Transfer through Controlled Manipulationscited by 0
- 2024Assessing the Role of Lexical Semantics in Cross-lingual Transfer through Controlled Manipulations14 Aug 2024
- 2023Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering20 Oct 2023
- 2023MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset8 May 2023
- 2022Word-order typology in Multilingual BERT: A case study in subordinate-clause detection24 May 2022