To Plan Or Not To Plan? Discourse Planning In Slot-Value Informed Sequence To Sequence Models For Language Generation

Natural language generation for task-oriented dialogue systems aims to effectively realize system dialogue actions. All natural language generators (NLGs) must realize grammatical, natural and appropriate output, but in addition. generators for task oriented dialogue must faithfully perform a specific dialogue act that conveys specific semantic information, as dictated by the dialogue policy of the system dialogue manager. Most previous work on deep learning methods for task-oriented NLG assumes that generation output can be an utterance skeleton. Utterances are delexicalized, with variable names for slots, which are then replaced with actual values as part of post-processing. However, the value of slots do. in fact, influence the lexical selection in the surrounding context as well as the overall sentence plan. To model this effect, we investigate sequence-to-sequence (seq2seq) models in which slot values are included as part of the input sequence and the output surface form. Furthermore. we study whether a separate sentence planning module that decides on grouping of slot value mentions as input to the seq2seq model results in more natural sentences than a seq2seq model that aims to jointly learn the plan and the surface realization.