text: The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.
  @cite_35: A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH's template-based generator even for quite complex information presentations.
  @cite_1: 
  @cite_11: In the past few years, as the number of dialogue systems has increased, there has been an increasing interest in the use of natural language generation in spoken dialogue. Our research assumes that trainable natural language generation is needed to support more flexible and customized dialogues with human users. This paper focuses on methods for automatically training the sentence planning module of a spoken language generator. Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e., the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. The paper first presents SPoT, a trainable sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. Our methodology is unique in neither depending on hand-crafted rules nor on the existence of a domain-specific corpus. SPoT first randomly generates a candidate set of sentence plans and then selects one. We show that SPoT learns to select a sentence plan whose rating on average is only 5 worse than the top human-ranked sentence plan. We then experimentally evaluate SPoT by asking human judges to compare SPoT's output with a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. We show that SPoT performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.
summary: Conventional approaches to NLG typically divide the task into sentence planning, and surface realisation. Sentence planning maps input semantic symbols into an intermediary tree-like or template structure representing the utterance, then surface realisation converts the intermediate structure into the final text @cite_11 @cite_35 @cite_1 . As noted above, one of the first statistical NLG methods that requires almost no handcrafting or semantic alignments was an n-gram based approach by . later addressed the limitations of n-gram LMs in the over-generation phase by using a more sophisticated generator based on a syntactic dependency tree.