# Generative Entity Typing with Curriculum Learning

arXiv: https://arxiv.org/abs/2210.02914 · HTML: https://mlsanity.com/paper/2210.02914/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021)
- [Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model](https://mlsanity.com/paper/2106.04098/) (2021)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [Reinforcement Learning based Curriculum Optimization for Neural Machine Translation](https://mlsanity.com/paper/1903.00041/) (2019)
- [Learning to Denoise Distantly-Labeled Data for Entity Typing](https://mlsanity.com/paper/1905.01566/) (2019)
- [A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification](https://mlsanity.com/paper/2010.02053/) (2020)
- [Deep Short Text Classification with Knowledge Powered Attention](https://mlsanity.com/paper/1902.08050/) (2019)
- [Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing](https://mlsanity.com/paper/1903.02591/) (2019)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Learning Dynamic Context Augmentation for Global Entity Linking](https://mlsanity.com/paper/1909.02117/) (2019)
- [Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations](https://mlsanity.com/paper/2106.02974/) (2021)
- [Hierarchical Entity Typing via Multi-level Learning to Rank](https://mlsanity.com/paper/2004.02286/) (2020)
- [Neural Architectures for Fine-grained Entity Type Classification](https://mlsanity.com/paper/1606.01341/) (2016)
- [Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations](https://mlsanity.com/paper/2004.03554/) (2020)
- [A Chinese Corpus for Fine-grained Entity Typing](https://mlsanity.com/paper/2004.08825/) (2020)
- [Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention](https://mlsanity.com/paper/2105.11119/) (2021)
- [Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds](https://mlsanity.com/paper/1804.08000/) (2018)
- [Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation](https://mlsanity.com/paper/1905.12198/) (2019)
- [Investigation on Data Adaptation Techniques for Neural Named Entity Recognition](https://mlsanity.com/paper/2110.05892/) (2021)

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

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [CodeKGC: Code Language Model for Generative Knowledge Graph Construction](https://mlsanity.com/paper/2304.09048/) (2023)
- [Causality-aware Concept Extraction based on Knowledge-guided Prompting](https://mlsanity.com/paper/2305.01876/) (2023)
- [Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains](https://mlsanity.com/paper/2401.13129/) (2024)
- [An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling](https://mlsanity.com/paper/2402.13534/) (2024)
- [Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models](https://mlsanity.com/paper/2406.10902/) (2024)
- [Large Language Model for Extracting Complex Contract Information in Industrial Scenes](https://mlsanity.com/paper/2507.06539/) (2025)
- [Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy](https://mlsanity.com/paper/2607.18984/) (2026)

## Abstract (arXiv)

Entity typing aims to assign types to the entity mentions in given texts. The traditional classification-based entity typing paradigm has two unignorable drawbacks: 1) it fails to assign an entity to the types beyond the predefined type set, and 2) it can hardly handle few-shot and zero-shot situations where many long-tail types only have few or even no training instances. To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM). However, PLMs tend to generate coarse-grained types after fine-tuning upon the entity typing dataset. Besides, we only have heterogeneous training data consisting of a small portion of human-annotated data and a large portion of auto-generated but low-quality data. To tackle these problems, we employ curriculum learning (CL) to train our GET model upon the heterogeneous data, where the curriculum could be self-adjusted with the self-paced learning according to its comprehension of the type granularity and data heterogeneity. Our extensive experiments upon the datasets of different languages and downstream tasks justify the superiority of our GET model over the state-of-the-art entity typing models. The code has been released on https://github.com/siyuyuan/GET.