To Plan or not to Plan? Discourse Planning in Slot-Value Informed Sequence to Sequence Models for Language Generation
To Plan or not to Plan? Discourse Planning in Slot-Value Informed Sequence to Sequence Models for Language Generation
复制标题
计划还是不计划？
DOI：
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发表时间：
2017
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影响因子：
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通讯作者：
Larry Heck
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作者：
Neha Nayak Kennard;Dilek Z. Hakkani;M. Walker;Larry Heck
面向任务的对话系统的自然语言生成旨在有效地实现系统的对话行为。所有的自然语言生成器（NLGs）必须实现语法，自然和适当的输出，但除此之外，面向任务的对话生成器必须忠实地执行特定的对话行为，传达特定的语义信息，如系统对话管理器的对话策略所指示的。针对面向任务的NLG的深度学习方法的大多数先前工作假设生成输出可以是话语骨架。语句是非词汇化的，槽的变量名，然后作为后处理的一部分，用实际值替换。然而，槽的价值，事实上，影响周围的语境中的词汇选择以及整体的句子计划。为了模拟这种效果，我们调查序列到序列（seq2seq）模型，其中槽值作为输入序列和输出表面形式的一部分。此外，我们研究是否一个单独的句子规划模块，决定分组槽值提到作为输入的seq2seq模型的结果比seq2seq模型，旨在共同学习的计划和表面实现更自然的句子。
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 taskoriented 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.