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Found 14 papers, 2 papers with code
Learning to Ask for Conversational Machine Learning
Natural language has recently been explored as a new medium of supervision for training machine learning models.
Look-up and Adapt: A One-shot Semantic Parser
In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch.
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds
Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge.
Learning to Learn Semantic Parsers from Natural Language Supervision
As humans, we often rely on language to learn language.
LIA: A Natural Language Programmable Personal Assistant
We present LIA, an intelligent personal assistant that can be programmed using natural language.
Zero-shot Learning of Classifiers from Natural Language Quantification
Humans can efficiently learn new concepts using language.
Joint Concept Learning and Semantic Parsing from Natural Language Explanations
Natural language constitutes a predominant medium for much of human learning and pedagogy.
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Second, we use this memory model to develop a stochastic model for spaced repetition systems.
Latent Skill Embedding for Personalized Lesson Sequence Recommendation
In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments.
