# TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit

arXiv: https://arxiv.org/abs/2401.07525 · HTML: https://mlsanity.com/paper/2401.07525/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://mlsanity.com/paper/2006.03236/) (2020)
- [TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning](https://mlsanity.com/paper/2106.06273/) (2021)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data](https://mlsanity.com/paper/2106.03096/) (2021)
- [Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network](https://mlsanity.com/paper/2009.13299/) (2020)
- [On the importance of pre-training data volume for compact language models](https://mlsanity.com/paper/2010.03813/) (2020)
- [Text-to-Image Generation for Abstract Concepts](https://mlsanity.com/paper/2309.14623/) (2023)
- [TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2011.14638/) (2020)
- [Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective](https://mlsanity.com/paper/2105.08007/) (2021)
- [GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2205.11139/) (2022)
- [Deep Natural Language Processing for LinkedIn Search](https://mlsanity.com/paper/2108.13300/) (2021)

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

- [LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation](https://mlsanity.com/paper/2510.05490/) (2025)

## Abstract (arXiv)

Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained large language models have further enhanced the effectiveness by leveraging richer textual information in user profiles and job descriptions apart from user behavior features and job metadata. However, the general domain-oriented design struggles to capture the unique structural information within user profiles and job descriptions, leading to a loss of latent semantic correlations. We propose TAROT, a hierarchical multitask co-pretraining framework, to better utilize structural and semantic information for informative text embeddings. TAROT targets semi-structured text in profiles and jobs, and it is co-pretained with multi-grained pretraining tasks to constrain the acquired semantic information at each level. Experiments on a real-world LinkedIn dataset show significant performance improvements, proving its effectiveness in person-job fit tasks.