Ai is now making impact on the traditional industries such as recruitment. CEO of a young AI startup in Vietnam had introduced the solution at Vietnam Frontier Summit 2019.

1. How AI will innovate
recruitment?
Nguyen Hoang Trong Viet
CEO Techainer
Vietnam Frontier Summit 2019

2. Nguyen Trong Hoang Viet
- CEO Techainer
- RubikAI Lecturer
- CTO Uland
- AI Team Lead - Cinnamon AI Lab
Techainer - AI Solution to automate Business
- HRChain: AI enhance recruitment flow
- MLChain: AI made easy
- Website: techainer.com
About me

3. AI in recruitment
Knowledge acquisition from CV
Agenda
01
02

4. Reward is huge for startup who invent a pain killer
Reference:
Global Recruitment Insights and Data
Crunchbase research
HR Technology Market 2019: Disruption Ahead
Estimated global market
size of recruitment
Investment in recruitment
startup in 2017 & 2018
$ 200 B
$ 1.3 B
Top priority for recruitment

5. Data
enrichment
How AI will impact recruitment?
Recruitment = Sourcing + Selection + Interview + Assessment + Offering
CV - JD
matching
Chatbot
interview
Candidate
ranking
Offer
optimizing

6. Part II
Knowledge acquisition from CV

7. Overview
Extracted result
Name
William
Smith
Address New York
Phoner 0123 456 678
University Standford
… …
Raw CV
CVChain
Profile
Education
Experience
Certification
Skills
...

8. CVChain Value Proposition
Saving TimeSaving Cost
Higher employee
engagement
With CVChain, your talent acquisition team to
performer much more efficiently

9. Challenges
Normal case Complex Table Non-Border Table Multi-column
- Multilingual
- Free format
- Contextual
- Big Data

10. Processing Flow
Super Complex Structure

11. Tokenizer
Tôi thích học các kiến thức trong AI. Tôi thích học các kiến_thức trong AI.
Tokenize[1]
N G U Y E N V A N D O N G K H A N G
T H Ô N G T I N C Á N H Â N
V Ũ T H U T H Ủ Y
NGUYENVANDONGKHANG
THÔNGTINCÁNHÂN
VŨTHUTHUỶ
NGUYEN VAN DONG KHANG
THÔNG TIN CÁ NHÂN
VŨ THU THUỶ Patterns
Dictionary
[1] Coc coc Tokenizer, https://github.com/coccoc/coccoc-tokenizer

12. Embeddings
[1] Akbik, Alan and Blythe, Duncan, Vollgraf, Roland, Contextual String Embeddings for Sequence Labeling, 27th International Conference on Computational Linguistics, 2018.
[2] Jeffrey Pennington, Richard Socher, Christopher D. Manning, Glove: Global vectors for word representation, 2014.
[3] Xinxiong Chen, Lei Xu, Zhiyuan Liu, Maosong Sun, Huanbo Luan, Joint learning of character and word embeddings, 2015.
Contextual String Embeddings[1]
Glove Embeddings[2]
Character Embeddings[3]
Training: 1M magazines + 2M CVs

13. Embeddings to Domain Specific Tasks
[1] Scott Menard. Applied logistic regression analysis, volume 106. SAGE publications, 2018.
[2] Alexandros Komninos and Suresh Manandhar. Dependency based embeddings for sentence classification tasks. In Proceedings of the 2016 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies, pages 1490–1500, 2016.
[3] Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360, 2016.
Text Regression[1]
GRU/LSTM
Linear
Text Classification[2]
GRU/LSTM
Linear + Softmax
Named Entity Recognition[3]

14. Master Key Detection
Text Classification model

15. Structure Analysis
- Table Detection
- Table Decomposition

16. Named Entity Recognition
Profile Blocks
Education Blocks
Experience Blocks
Certification Blocks
Skills Blocks
...
BiLSTM + CRF[1]
Entities
Compare to BERT[2]
, XLNet[3]
:
- Faster
- Acceptable speed on CPU
- Promising result by using trained language
model
- Work with any length >< 384 max length of
BERT
[1] Zhiheng Huang, Wei Xu, Kai Yu, Bidirectional LSTM-CRF Models for Sequence Tagging, 2015.
[2] Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.
[3] Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le, XLNet: Generalized Autoregressive Pretraining for Language Understanding, 2019.

17. Entity Relation Extraction
[1] Guo, Zhijiang and Zhang, Yan and Lu, Wei, Attention Guided Graph Convolutional Networks for Relation Extraction, 2019.
Attention Guided Graph Convolutional
Networks for Relation Extraction

18. Candidate Assessment
Name
Skills
Education
Experience
Gender
Gender Prediction
Industries
Positions (Level
included)
Appropriate jobs
Salary
Career path?

19. What next?
End2End Information Extraction
- Structure + Context understanding
- Faster than consecutively modules
- Easy to improve
Document Graph Structure Contextual Learning
A great journey is on going with promising roadmap

20. Does anyone have any
questions?
+84 363 568 384
admin@techainer.com
techainer.com
THANKS!
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“If you want to go fast, then go alone. If you want
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