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Found 8 papers, 2 papers with code
Mathematically Modeling the Lexicon Entropy of Emergent Language
We formulate a stochastic process, FiLex, as a mathematical model of lexicon entropy in deep learning-based emergent language systems.
Modeling Emergent Lexicon Formation with a Self-Reinforcing Stochastic Process
We introduce FiLex, a self-reinforcing stochastic process which models finite lexicons in emergent language experiments.
Recommendations for Systematic Research on Emergent Language
Emergent language is unique among fields within the discipline of machine learning for its open-endedness, not obviously presenting well-defined problems to be solved.
Shaped Rewards Bias Emergent Language
The second is shaped rewards which are designed specifically to make the task easier to learn by introducing biases in the learning process.
Case Study: Deontological Ethics in NLP
Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices.
Detecting Compromised Implicit Association Test Results Using Supervised Learning
An implicit association test is a human psychological test used to measure subconscious associations.
Using LSTMs to Model the Java Programming Language
Recurrent neural networks (RNNs), specifically long-short term memory networks (LSTMs), can model natural language effectively.
Precise but Natural Specification for Robot Tasks
We present Flipper, a natural language interface for describing high-level task specifications for robots that are compiled into robot actions.
