# PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation

arXiv: https://arxiv.org/abs/2101.11216 · HTML: https://mlsanity.com/paper/2101.11216/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://mlsanity.com/paper/1702.03859/) (2017)
- [Torch-Struct: Deep Structured Prediction Library](https://mlsanity.com/paper/2002.00876/) (2020)
- [Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages](https://mlsanity.com/paper/1909.09265/) (2019)
- [Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing](https://mlsanity.com/paper/1909.01482/) (2019)
- [Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank](https://mlsanity.com/paper/1909.02235/) (2019)
- [Low-Resource Syntactic Transfer with Unsupervised Source Reordering](https://mlsanity.com/paper/1903.05683/) (2019)
- [Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections](https://mlsanity.com/paper/1906.02656/) (2019)

## Built on by (most cited first, 6 of 6)

- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- ["Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks](https://mlsanity.com/paper/2104.08384/) (2021)
- [Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing](https://mlsanity.com/paper/2110.08538/) (2021)
- [Cross-lingual Transfer Learning for Javanese Dependency Parsing](https://mlsanity.com/paper/2401.12072/) (2024)
- [Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data](https://mlsanity.com/paper/2110.03866/) (2021)
- [Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization](https://mlsanity.com/paper/2210.09428/) (2022)

## Abstract (arXiv)

Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple `direct transfer' of a learned model based on a multilingual input encoding has provided a strong benchmark. This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language. The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer. With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach. We provide analyses of the choice of source languages for multi-source transfer, and the advantage of non-projective parsing. Our code is available online.