Before revision:  These experiments show that the multi-timescale model successfully learns representations at the desired timescales, and that the distribution includes longer timescales than a standard LSTM. Further, information about high-,mid-, and low-frequency words is routed preferentially through units with the appropriate timescales.
After revision:  These experiments show that the multi-timescale model selectively routes information about different types of words through units with the appropriate timescales.