Summary of Tarot: a Hierarchical Framework with Multitask Co-pretraining on Semi-structured Data Towards Effective Person-job Fit, by Yihan Cao et al.
TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit
by Yihan Cao, Xu Chen, Lun Du, Hao Chen, Qiang Fu, Shi Han, Yushu Du, Yanbin Kang, Guangming Lu, Zi Li
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TAROT framework is designed to improve the effectiveness of online recruitment platforms by leveraging richer textual information in user profiles and job descriptions. Unlike general domain-oriented designs, TAROT targets semi-structured text in profiles and jobs, and co-pretrains with multi-grained pretraining tasks to constrain the acquired semantic information at each level. This hierarchical multitask co-pretraining framework is shown to significantly improve performance on person-job fit tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The TAROT framework helps online recruitment platforms work better by using more information from user profiles and job descriptions. It’s like a special kind of learning that helps computers understand the relationships between people and jobs. By using this new approach, recruiters can find better matches for job openings, making it easier to fill positions. |
Keywords
» Artificial intelligence » Pretraining
