Alireza Mahmoudi Kamelabad

Alireza Mahmoudi Kamelabad
KTH Royal Institute of Technology | KTH · Department of Speech, Music and Hearing (TMH)

Master of Science
Robot Assisted Language Learning

About

2
Publications
244
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Citations
Introduction
Computational Modeling of Language Acquisition
Skills and Expertise
Natural Language Processing
Text Mining
Machine Learning
Data Mining and Knowledge Discovery
Supervised Learning
Unsupervised Learning
Knowledge Representation
Semi-Supervised Learning
Computational Intelligence
Python
Additional affiliations
April 2021 - present
KTH Royal Institute of Technology
KTH Royal Institute of Technology
  • Department of Speech, Music and Hearing (TMH)
  • Stockholm, Sweden
Position
  • PhD Student
December 2019 - February 2020
Tilburg University
Tilburg University
  • Tilburg Center for Cognition and Communication "TiCC"
  • Tilburg, Netherlands
Position
  • Teacher Assistant
October 2019 - February 2020
Tilburg University
Tilburg University
  • Tilburg Center for Cognition and Communication "TiCC"
  • Tilburg, Netherlands
Position
  • Research Intern
Education
April 2021 - April 2025
KTH Royal Institute of Technology
KTH Royal Institute of Technology
Field of study
  • Computer Science
September 2018 - October 2020
Università degli Studi di Trento
Università degli Studi di Trento
Field of study
  • Cognitive Science, Language and Multimodal Interaction
September 2017 - August 2018
Università degli Studi di Trento
Università degli Studi di Trento
Field of study
  • Human-Computer Interaction

Publications

Publications (2)
Figure 1: Comprehension train and test accuracy during training.
Figure 2: Production train and test accuracy during training. Please...
Average accuracy on test data
Curiosity in self-supervised active word learning
Conference Paper
Full-text available
  • Sep 2020
Cognitive models of child word learning in general, and cross-situational models in particular, characterize the learner as a passive observer. But children are curious and actively participate in verbal and non-verbal communication, and often introduce new topics which parents are likely to follow up (Bloom et al., 1996). We investigate the potent...
Figure 4: Overlapping choices of the different selection mechanisms for...
Average accuracy and standard deviation on test data
Active Word Learning through Self-supervision
Conference Paper
Full-text available
  • Jul 2020
Models of cross-situational word learning typically characterize the learner as a passive observer, but a language learning child can actively participate in verbal and non-verbal communication. We present a computational study of cross-situational word learning to investigate whether a curious word learner who actively influences linguistic input...

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