# Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing

arXiv: https://arxiv.org/abs/1909.01482 · HTML: https://mlsanity.com/paper/1909.01482/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction](https://mlsanity.com/paper/2203.08308/) (2022)
- [Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training](https://mlsanity.com/paper/2104.08645/) (2021)
- [Evaluating the Values of Sources in Transfer Learning](https://mlsanity.com/paper/2104.12567/) (2021)
- [Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages](https://mlsanity.com/paper/2107.10181/) (2021)
- [PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation](https://mlsanity.com/paper/2101.11216/) (2021)
- [Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing](https://mlsanity.com/paper/2110.08538/) (2021)
- [On the Relation between Syntactic Divergence and Zero-Shot Performance](https://mlsanity.com/paper/2110.04644/) (2021)
- [A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning](https://mlsanity.com/paper/2210.09934/) (2022)
- [Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering](https://mlsanity.com/paper/2310.13583/) (2023)
- [Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer](https://mlsanity.com/paper/2309.10891/) (2023)
- [Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization](https://mlsanity.com/paper/2210.09428/) (2022)

## Abstract (arXiv)

Prior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlooks the potential of leveraging linguistic properties of the languages to facilitate the transfer. In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially. Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-wise constraints to guide the inference process during the test time. We adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints. Experiments show that the Lagrangian relaxation and posterior regularization inference improve the performances on 15 and 17 out of 19 target languages, respectively. The improvements are especially significant for target languages that have different word order features from the source language.