Teach Your Robot Your Language! Trainable Neural Parser for Modeling Human Sentence Processing: Examples for 15 Languages

Xavier Hinaut, Johannes Twiefel
IEEE Trans. Cogn. Dev. Syst.. 2020-06-01; 12(2): 179-188
DOI: 10.1109/TCDS.2019.2957006

We present a recurrent neural network (RNN) that performs thematic role assignment and can be used for human–robot interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g., predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this article, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in 15 languages, namely English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay, and Mandarin Chinese. Moreover, in the corpora, we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that: 1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature and 2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language-independent aspect makes it a good candidate for modeling human sentence processing. Finally, we discuss the potential implementation of the model in a grounded robotic architecture.

Auteurs Bordeaux Neurocampus