Using Machine Learning techniques to boost language acquisition

A Quick Summary:

I'm trying to see if it's possible to predict the words a toddler may learn next, by gathering information about their current and past vocabularies and applying a machine learning model based on Spatio Temporal Graph Neural Networks. By informing the model with multiple datasets about connections in the English language (e.g. Sensorimotor, Psycholinguistic, Semantic, Phonological, Word Association norms) and building a predictive model that combines that data with information about how a child's vocabulary changes over time, I'm hoping to match or improve accuracy based on an existing technique, using standard neural networks. We know that neural networks can predict upcoming vocabulary [1] - for roughly the following month, so I am looking to see how an approach using a different technique may compare.

In more detail:

Children’s level of language acquisition from around the age of two years upwards has been shown to be positively correlated with their later performance at school[2, 3]. It follows that one way to improve a child’s future school performance would be to encourage him or her to acquire language as early as possible. Children prefer to learn words that they can categorise with other words that they already know [4] – firstly through a similarity of shape (the 'shape bias'[5]) and then though other more complex associations[6] as the child's mind creates more categories. It follows, then, that if a system used by the parent – such as a mobile device based application – could be used to log information about the child's language development, it could also give advice to the parent about which words to encourage the child to learn next, those words having been judged by the system to be the best for expanding the child's vocabulary. This may lead to the child learning more language at an earlier age. This could have particular application to children who are already in groups likely to experience a delay in language acquisition (by virtue of demographic factors[7] or for medical reasons).

However, no two children are the same. A child living on a farm may have different environmental influences on their vocabulary compared to a child living in an inner-city area. Two words that may be closely semantically linked in one child's mind may not be linked at all in the mind of another.

Contemporary machine learning techniques combined with cheaper and higher-performance computer hardware have shown great success in moving the field of pattern recognition forward in recent years, and are being used to improve many applications of artificial intelligence. Recent advances include individual patient health prediction based on collective health record data [9, 10, 11]. In general terms, diagnosing possible health problems in a patient based on that patient’s health records and history – with access to a large volume of other patient health data – is analogous to our problem; although instead of predicting the likelihood of a particular health issue, we are predicting the likelihood of the child learning a particular group of words. In our proposed system, the use of graph neural network techniques should suggest the best words to learn next, based on the child's growing vocabulary. This could lead to a tool to help to boost child language acquisition.

If successful, the system may also have applications for non-typical subjects such as children with Autism Spectrum Disorder. Ultimately it may even have applications for individuals with brain injuries affecting speech and language.

Supervisors:

Dr Floriana Grasso
Dr Terry Payne

Research Question:

Using contemporary machine learning methods and contemporary datasets, is it possible to create a computational model that will predict the words that a given typically-developing child – represented by static and time-varying environmental and vocabulary data – is most likely to learn next?

References

[1] N. Beckage, PhD Thesis. University Of Colorado.
[2] D. Bleses, G. Makransky, P. S. Dale, A. Hojen, and B. A. Ari, “Early productive vocabulary predicts academic achievement 10 years later,” Applied Psycholinguistics, pp. 1–16, 2016.
[3] D. Walker, C. Greenwood, and B. Hart, “Prediction of School Outcomes Based on Early Language Production and Socioeconomic Factors,” Child Development, vol. 65, no. 2, pp. 606–621, 1994.
[4] A. Borovsky and J. L. Elman, “Language input and semantic categories: A relation between cognition and early word learning,” Journal of Child Language, vol. 33, no. 4, pp. 759–790, 2006.
[5] L. Gershkoff-Stowe and L. B. Smith, “Shape and the first hundred nouns,” Child Development, vol. 75, no. 4, pp. 1098–1114, 2004.
[6] G. Diesendruck, “Mechanisms of Word Learning,” in Blackwell Handbook of Language Development, ch. 13, pp. 257–276, 2008.
[7] B. Hart and T. R. Risley, Meaningful differences in the everyday experience of young American children. Baltimore,MD: Paul H Brookes Publishing, 1995.
[8] Y. Bengio, “Learning Deep Architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.
[9] T. Pham, T. Tran, D. Phung, and S. Venkatesh, “DeepCare: A Deep Dynamic Memory Model for Predictive Medicine,” in Advances in Knowledge Discovery and Data Mining: PAKDD 2016 Proceedings (J. Bailey, , L. Khan, , T. Washio, , G. Dobbie, , Z. J. Huang, , and R. Wang, eds.), no. i, pp. 30–41, Springer International Publishing, 2016.
[10] R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley, “Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records,” Scientific Reports, vol. 6, 26094, no. April, 2016.
[11] Z. Liang, G. Zhang, J. X. Huang, and Q. V. Hu, “Deep learning for healthcare decision making with EMRs,” Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, no. Cm, pp. 556–559, 2014.
[12] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing Atari with Deep Reinforcement Learning,” NIPS Deep Learning Workshop, 2013.
[13] B. Bakker, “Reinforcement Learning with Long Short-Term Memory,” Nips, 2002.
[14] M. Hausknecht and P. Stone, “Deep Recurrent Q-Learning for Partially Observable MDPs,” arXiv preprint arXiv:1507.06527, 2015.