Interpretable embeddings to understand computing careers
We propose an approach for analyzing and comparing curricula of study programs in higher education. Pre-trained word embeddings are fine-tuned in a study program classification task, where each curriculum is represented by the names and content of its courses. By combining metric learning with a novel course-guided attention mechanism, our method obtains more accurate curriculum representations than strong baselines. Experiments on a new dataset containing curricula of computing programs demonstrate the interpretability power of our approach via attention weights, topic modeling, and embeddings visualizations. We also present a use case that compares computing study programs in the US and Latin America and showcase the capabilities of our method for identifying similarities and differences in topics of study in curricula from different countries.
Code (0)
등록된 구현이 없습니다.
Tasks
Metric LearningWord EmbeddingsSimilar Papers 제목 키워드 기반
JobMatchAI An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI
Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonli…
Skill GeneralizationKnowledge GraphsMatch made by BERT? Towards Interpretable Paper-Reviewer Assignments in NLP
Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has been mostly approached simply as an automa…
Word Equations: Inherently Interpretable Sparse Word Embeddingsthrough Sparse Coding
Word embeddings are a powerful natural language processing technique, but they are extremely difficult to interpret. To enable interpretable NLP models, we create vectors where each dimension is inherently interpretable.…
Word EmbeddingsWord Equations: Inherently Interpretable Sparse Word Embeddings through Sparse Coding
Word embeddings are a powerful natural language processing technique, but they are extremely difficult to interpret. To enable interpretable NLP models, we create vectors where each dimension is inherently interpretable.…
Word EmbeddingsA nonparametric framework for inferring orders of categorical data from category-real ordered pairs
Given a dataset of careers and incomes, how large a difference of income between any pair of careers would be? Given a dataset of travel time records, how long do we need to spend more when choosing a public transportati…