# Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection

arXiv: https://arxiv.org/abs/1607.01133 · HTML: https://mlsanity.com/paper/1607.01133/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2016.

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

- [KinyaBERT: a Morphology-aware Kinyarwanda Language Model](https://mlsanity.com/paper/2203.08459/) (2022)
- [Analysing the Noise Model Error for Realistic Noisy Label Data](https://mlsanity.com/paper/2101.09763/) (2021)
- [Unraveling Social Perceptions & Behaviors towards Migrants on Twitter](https://mlsanity.com/paper/2112.06642/) (2021)
- [Graph-Based Multilingual Label Propagation for Low-Resource Part-of-Speech Tagging](https://mlsanity.com/paper/2210.09840/) (2022)
- [Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment](https://mlsanity.com/paper/2210.04141/) (2022)
- [Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Distant Supervision](https://mlsanity.com/paper/2303.01912/) (2023)
- [Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches](https://mlsanity.com/paper/2309.12224/) (2023)
- [LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models](https://mlsanity.com/paper/2411.13453/) (2024)

## Abstract (arXiv)

Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low-resource languages, Malagasy and Kinyarwanda.