# Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging

arXiv: https://arxiv.org/abs/1808.09733 · HTML: https://mlsanity.com/paper/1808.09733/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling](https://mlsanity.com/paper/2103.07792/) (2021)
- [Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features](https://mlsanity.com/paper/2310.06458/) (2023)
- [Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data](https://mlsanity.com/paper/2109.04319/) (2021)
- [Learning Action Conditions from Instructional Manuals for Instruction Understanding](https://mlsanity.com/paper/2205.12420/) (2022)
- [Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging](https://mlsanity.com/paper/2210.09840/) (2022)
- [Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text](https://mlsanity.com/paper/2208.01814/) (2022)
- [High-Resource Methodological Bias in Low-Resource Investigations](https://mlsanity.com/paper/2211.07534/) (2022)
- [Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages](https://mlsanity.com/paper/2506.17715/) (2025)

## Abstract (arXiv)

We introduce DsDs: a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.