This approach is related to the 'numerically-grounded' language model (Spithourakis et al., 2016), except that we scale the politeness label embedding by its corresponding politeness score, rather than concatenating the two as input to the LSTM.
3 the LFT model is able to simultaneously produce polite, neutral and rude responses depending on the prepended label, similar to recent multilabel, multi-space, and zero-shot machine translation work using language identity or style labels (Sennrich et al., 2016a;Johnson et al., 2017;Ghosh et al., 2017).