# Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text

arXiv: https://arxiv.org/abs/2208.01814 · HTML: https://mlsanity.com/paper/2208.01814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Universal Dependency Parsing from Scratch](https://mlsanity.com/paper/1901.10457/) (2019)
- [Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss](https://mlsanity.com/paper/1604.05529/) (2016)
- [Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging](https://mlsanity.com/paper/1808.09733/) (2018)

## Built on by (most cited first, 1 of 1)

- [The UD-NewsCrawl Treebank: Reflections and Challenges from a Large-scale Tagalog Syntactic Annotation Project](https://mlsanity.com/paper/2505.20428/) (2025)

## Abstract (arXiv)

The grammatical analysis of texts in any written language typically involves a number of basic processing tasks, such as tokenization, morphological tagging, and dependency parsing. State-of-the-art systems can achieve high accuracy on these tasks for languages with large datasets, but yield poor results for languages which have little to no annotated data. To address this issue for the Tagalog language, we investigate the use of alternative language resources for creating task-specific models in the absence of dependency-annotated Tagalog data. We also explore the use of word embeddings and data augmentation to improve performance when only a small amount of annotated Tagalog data is available. We show that these zero-shot and few-shot approaches yield substantial improvements on grammatical analysis of both in-domain and out-of-domain Tagalog text compared to state-of-the-art supervised baselines.