# Modeling sequential annotations for sequence labeling with crowds

arXiv: https://arxiv.org/abs/2209.09430 · HTML: https://mlsanity.com/paper/2209.09430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 20 Sep 2022.

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

- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling](https://mlsanity.com/paper/1910.04289/) (2019)
- [CRF-CNN: Modeling Structured Information in Human Pose Estimation](https://mlsanity.com/paper/1611.00468/) (2016)
- [Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.04621/) (2019)

## Abstract (arXiv)

Crowd sequential annotations can be an efficient and cost-effective way to build large datasets for sequence labeling. Different from tagging independent instances, for crowd sequential annotations the quality of label sequence relies on the expertise level of annotators in capturing internal dependencies for each token in the sequence. In this paper, we propose Modeling sequential annotation for sequence labeling with crowds (SA-SLC). First, a conditional probabilistic model is developed to jointly model sequential data and annotators' expertise, in which categorical distribution is introduced to estimate the reliability of each annotator in capturing local and non-local label dependency for sequential annotation. To accelerate the marginalization of the proposed model, a valid label sequence inference (VLSE) method is proposed to derive the valid ground-truth label sequences from crowd sequential annotations. VLSE derives possible ground-truth labels from the token-wise level and further prunes sub-paths in the forward inference for label sequence decoding. VLSE reduces the number of candidate label sequences and improves the quality of possible ground-truth label sequences. The experimental results on several sequence labeling tasks of Natural Language Processing show the effectiveness of the proposed model.