Competence-Level Prediction and Resume & Job Description Matching Using
Context-Aware Transformer Models
- URL: http://arxiv.org/abs/2011.02998v1
- Date: Thu, 5 Nov 2020 17:47:03 GMT
- Title: Competence-Level Prediction and Resume & Job Description Matching Using
Context-Aware Transformer Models
- Authors: Changmao Li, Elaine Fisher, Rebecca Thomas, Steve Pittard, Vicki
Hertzberg, Jinho D. Choi
- Abstract summary: A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators.
A high Kappa score of 61% is achieved for inter-annotator agreement.
- Score: 13.302702823447476
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a comprehensive study on resume classification to reduce
the time and labor needed to screen an overwhelming number of applications
significantly, while improving the selection of suitable candidates. A total of
6,492 resumes are extracted from 24,933 job applications for 252 positions
designated into four levels of experience for Clinical Research Coordinators
(CRC). Each resume is manually annotated to its most appropriate CRC position
by experts through several rounds of triple annotation to establish guidelines.
As a result, a high Kappa score of 61% is achieved for inter-annotator
agreement. Given this dataset, novel transformer-based classification models
are developed for two tasks: the first task takes a resume and classifies it to
a CRC level (T1), and the second task takes both a resume and a job description
to apply and predicts if the application is suited to the job T2. Our best
models using section encoding and multi-head attention decoding give results of
73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are
mostly made among adjacent CRC levels, which are hard for even experts to
distinguish, implying the practical value of our models in real HR platforms.
Related papers
- EquiBench: Benchmarking Code Reasoning Capabilities of Large Language Models via Equivalence Checking [54.354203142828084]
We present the task of equivalence checking as a new way to evaluate the code reasoning abilities of large language models.
We introduce EquiBench, a dataset of 2400 program pairs spanning four programming languages and six equivalence categories.
Our evaluation of 17 state-of-the-art LLMs shows that OpenAI o3-mini achieves the highest overall accuracy of 78.0%.
arXiv Detail & Related papers (2025-02-18T02:54:25Z) - Preference Optimization for Reasoning with Pseudo Feedback [100.62603571434167]
We introduce a novel approach to generate pseudo feedback for reasoning tasks by framing the labeling of solutions as an evaluation against associated test cases.
We conduct experiments on both mathematical reasoning and coding tasks using pseudo feedback for preference optimization, and observe improvements across both tasks.
arXiv Detail & Related papers (2024-11-25T12:44:02Z) - Large Language Models in the Workplace: A Case Study on Prompt
Engineering for Job Type Classification [58.720142291102135]
This case study investigates the task of job classification in a real-world setting.
The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
arXiv Detail & Related papers (2023-03-13T14:09:53Z) - Predicting Job Titles from Job Descriptions with Multi-label Text
Classification [0.0]
We propose the multi-label classification approach for predicting relevant job titles from job description texts.
We implement the Bi-GRU-LSTM-CNN with different pre-trained language models to apply for the job titles prediction problem.
arXiv Detail & Related papers (2021-12-21T09:31:03Z) - Assessing Data Efficiency in Task-Oriented Semantic Parsing [54.87705549021248]
We introduce a four-stage protocol which gives an approximate measure of how much in-domain "target" data a requires to achieve a certain quality bar.
We apply our protocol in two real-world case studies illustrating its flexibility and applicability to practitioners in task-oriented semantic parsing.
arXiv Detail & Related papers (2021-07-10T02:43:16Z) - LRG at SemEval-2021 Task 4: Improving Reading Comprehension with
Abstract Words using Augmentation, Linguistic Features and Voting [0.6850683267295249]
Given a fill-in-the-blank-type question, the task is to predict the most suitable word from a list of 5 options.
We use encoders of transformers-based models pre-trained on the masked language modelling (MLM) task to build our Fill-in-the-blank (FitB) models.
We propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc.
arXiv Detail & Related papers (2021-02-24T12:33:12Z) - NEMO: Frequentist Inference Approach to Constrained Linguistic Typology
Feature Prediction in SIGTYP 2020 Shared Task [83.43738174234053]
We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features.
Our best configuration achieved the micro-averaged accuracy score of 0.66 on 149 test languages.
arXiv Detail & Related papers (2020-10-12T19:25:43Z) - IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template
Reconstruction Strategy for ComVE [13.334749848189826]
We formalize the subtasks into the multiple-choice question answering format and construct the input with the prompt templates.
Experimental results show that our approaches achieve significant performance compared with the baseline systems.
Our approaches secure the third rank on both official test sets of the first two subtasks with an accuracy of 96.4 and an accuracy of 94.3 respectively.
arXiv Detail & Related papers (2020-07-02T06:59:53Z) - On the Inference of Soft Biometrics from Typing Patterns Collected in a
Multi-device Environment [47.37893297206786]
In this paper, we study the inference of gender, major/minor, typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment.
For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers.
The results are promising considering the variety of application scenarios that we have listed in this work.
arXiv Detail & Related papers (2020-06-16T20:25:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.