Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model

Sepideh Sadeghi and Matthias Scheutz

We present an incremental and memory-limited Bayesian cross-situational word learning model and evaluate the model in terms of its functional performance and its sensitivity to input order. We show that the functional performance of our sub-optimal model on corpus data is close to that of its optimal counterpart (Frank et al., 2009), while only the sub-optimal model is capable of predicting the input order effects reported in experimental studies.

@inproceedings{sadeghi2018coling,
  title={Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model},
  author={Sepideh Sadeghi and Matthias Scheutz},
  year={2018},
  booktitle={proceedings of the 27th International Conference on Computational Linguistics (COLING 2018)},
  url={https://hrilab.tufts.edu/publications/sadeghi2018coling.pdf}
}