Word-order Biases in Deep-agent Emergent Communication
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni · 2019
Sequence-processing neural networks led to remarkable progress on many NLP tasks.As a consequence, there has been increasing interest in understanding to what extent they process language as humans do.We aim here to uncover which biases such models display with respect to "natural" word-order constraints.We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies.We study how the controlled characteristics of our miniature languages affect individual learning and their stability across multiple network generations.The results draw a mixed picture.On the one hand, neural networks show a strong tendency to avoid long-distance dependencies.On the other hand, there is no clear preference for the efficient, non-redundant encoding of information that is widely attested in natural language.We thus suggest inoculating a notion of "effort" into neural networks, as a possible way to make their linguistic behavior more humanlike.