Search Results for author:
Found 22 papers, 16 papers with code
Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models
For the LLM to generate actionable plans, scene context must be provided, often through a map.
Learning to Open and Traverse Doors with a Legged Manipulator
Using doors is a longstanding challenge in robotics and is of significant practical interest in giving robots greater access to human-centric spaces.
Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers
Overall, our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting.
Computational Job Market Analysis with Natural Language Processing
[Abridged Abstract] Recent technological advances underscore labor market dynamics, yielding significant consequences for employment prospects and increasing job vacancy data across platforms and languages.
Can Humans Identify Domains?
Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance.
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries.
Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings
Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis.
Rethinking Skill Extraction in the Job Market Domain using Large Language Models
Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes.
JOBSKAPE: A Framework for Generating Synthetic Job Postings to Enhance Skill Matching
Recent approaches in skill matching, employing synthetic training data for classification or similarity model training, have shown promising results, reducing the need for time-consuming and expensive annotations.
Entity Linking in the Job Market Domain
In this work, we are the first to explore EL in this domain, specifically targeting the linkage of occupational skills to the ESCO taxonomy (le Vrang et al., 2014).
NNOSE: Nearest Neighbor Occupational Skill Extraction
The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text.
The Responsible Development of Automated Student Feedback with Generative AI
The task is considered from both a development and maintenance perspective, considering how automated feedback tools will evolve and be used over time.
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain
The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification.
Evidence > Intuition: Transferability Estimation for Encoder Selection
With the increase in availability of large pre-trained language models (LMs) in Natural Language Processing (NLP), it becomes critical to assess their fit for a specific target task a priori - as fine-tuning the entire space of available LMs is computationally prohibitive and unsustainable.
Skill Extraction from Job Postings using Weak Supervision
Aggregated data obtained from job postings provide powerful insights into labor market demands, and emerging skills, and aid job matching.
Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning
Skill Classification (SC) is the task of classifying job competences from job postings.
SkillSpan: Hard and Soft Skill Extraction from English Job Postings
We introduce a BERT baseline (Devlin et al., 2019).
Experimental Standards for Deep Learning in Natural Language Processing Research
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well.
Cartography Active Learning
We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling.
De-identification of Privacy-related Entities in Job Postings
We present JobStack, a new corpus for de-identification of personal data in job vacancies on Stackoverflow.
The Effect of Translationese in Machine Translation Test Sets
The effect of translationese has been studied in the field of machine translation (MT), mostly with respect to training data.
Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate
The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish.
