Abstract
Streamlining the job-matching process requires robust and innovative solutions in today’s complex job market.
Understanding cutting-edge Natural Language Processing (NLP)
models is essential so that users receive the most appropriate
responses. This study aims to introduce a novel chatbot based on
the Transformer-based Text-to-Text Transfer Transformer (T5)
model and compare it to GPT-2 to address the challenges of
finding the best job based on job seekers’ preferences and also
compare both generated answers to realise the accuracy of each
model in job matching chatbot. GPT-2 and T5 both are excel
in natural language understanding tasks, enabling us to parse
and map user queries to many job preferences, ensuring a
comprehensive understanding of user skills, and also they can
provide a high rate of accuracy and performance in the natural
language tasks. Our methodology focuses on preference job
matching, which effectively bridges the gap between employers
and job seekers. Using context-based meanings of specific words
and new terms defined in the conversation, the model generates
responses based on user input. A seamless connection between
job seekers and potential employers is made possible by our
approach to Human Resources technology, which serves as a
more personalised, effective, and user-friendly job matching
system. The model tokenises words and generates test cases based
on them. By utilising NLP techniques, it will help to ensure that
all scenarios are taken into account.
Understanding cutting-edge Natural Language Processing (NLP)
models is essential so that users receive the most appropriate
responses. This study aims to introduce a novel chatbot based on
the Transformer-based Text-to-Text Transfer Transformer (T5)
model and compare it to GPT-2 to address the challenges of
finding the best job based on job seekers’ preferences and also
compare both generated answers to realise the accuracy of each
model in job matching chatbot. GPT-2 and T5 both are excel
in natural language understanding tasks, enabling us to parse
and map user queries to many job preferences, ensuring a
comprehensive understanding of user skills, and also they can
provide a high rate of accuracy and performance in the natural
language tasks. Our methodology focuses on preference job
matching, which effectively bridges the gap between employers
and job seekers. Using context-based meanings of specific words
and new terms defined in the conversation, the model generates
responses based on user input. A seamless connection between
job seekers and potential employers is made possible by our
approach to Human Resources technology, which serves as a
more personalised, effective, and user-friendly job matching
system. The model tokenises words and generates test cases based
on them. By utilising NLP techniques, it will help to ensure that
all scenarios are taken into account.
| Original language | English |
|---|---|
| Title of host publication | Proceedings 2023 16th International Conference on Developments in eSystems Engineering (DeSE) |
| Publisher | IEEE |
| ISBN (Electronic) | 9798350381344 |
| Publication status | Published - 21 Mar 2024 |
| Event | 16th International Conference on Developments in eSystems Engineering - Atlas University, Istanbul, Turkey Duration: 18 Dec 2023 → 20 Dec 2023 https://www.atlas.edu.tr/v1/en/the-16th-international-conference-on-developments-in-esystems-engineering-dese2023-took-place-at-our-atlas-valley-campus/ |
Conference
| Conference | 16th International Conference on Developments in eSystems Engineering |
|---|---|
| Abbreviated title | DeSE 2023 |
| Country/Territory | Turkey |
| City | Istanbul |
| Period | 18/12/23 → 20/12/23 |
| Internet address |