Search Results for author:
Found 15 papers, 6 papers with code
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits
We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students.
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems
Our personalized feedback can pinpoint correct and incorrect or missing phrases in student answers as well as guide them towards correct answer by asking a question in natural language.
A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions
AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning.
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval
We run UDA experiments on question generation and passage retrieval from the \textit{Natural Questions} domain to machine learning and biomedical domains.
Comparative Study of Learning Outcomes for Online Learning Platforms
Personalization and active learning are key aspects to successful learning.
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM
We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS).
Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Deep reinforcement learning has recently shown many impressive successes.
Model-based Reinforcement Learning
reinforcement-learning
+1
Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus
In this paper, we analyze neural network-based dialogue systems trained in an end-to-end manner using an updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
Generative Deep Neural Networks for Dialogue: A Short Review
Researchers have recently started investigating deep neural networks for dialogue applications.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.
Ranked #1 on
Dialogue Generation
on Ubuntu Dialogue (Activity)
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances.
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models.
