Abstract
We present a variation of the incremental and memory-limited algorithm in (Sadeghi et al., 2017) for Bayesian cross-situational word learning 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.- Anthology ID:
- C18-1268
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3170–3180
- Language:
- URL:
- https://aclanthology.org/C18-1268
- DOI:
- Cite (ACL):
- Sepideh Sadeghi and Matthias Scheutz. 2018. Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3170–3180, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model (Sadeghi & Scheutz, COLING 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/C18-1268.pdf