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Learning of dialogue states and language model of spoken information system

机译:对话状态的学习和语音信息系统的语言模型

摘要

In this invention dialogue states for a dialogue model are created using a training corpus of example human-human dialogues. Dialogue states are modelled at the turn level rather than at the move level, and the dialogue states are derived from the training corpus. ;The range of operator dialogue utterances is actually quite small in many services and therefore may be categorized into a set of predetermined meanings. This is an important assumption which is not true of general conversation, but is often true of conversations between telephone operators and people. ;Phrases are specified which have specific substitution and deletion penalties, for example the two phrases ‘I would like to’ and ‘can I’ may be specified as a possible substitution with low or zero penalty. Thus allows common equivalent phrases are given low substitution penalties. Insignificant phrases such as ‘erm’ are given low or zero deletion penalties.
机译:在本发明中,使用示例性人-人对话的训练语料来创建对话模型的对话状态。对话状态是在转弯级别而不是在移动级别建模的,并且对话状态是从训练语料库中得出的。 ;在许多服务中,操作员对话话语的范围实际上很小,因此可以归类为一组预定含义。这是一个重要的假设,对于一般对话而言并非如此,但对于电话运营商与人之间的对话而言,通常是正确的。 ;指定了具有特定替换和删除处罚的词组,例如“我想”两个词组。和“我可以吗?可以指定为低或零罚分的可能替代。因此,允许对常见的等价词组给予较低的替代惩罚。无关紧要的词组,例如’ erm’给予低或零的删除处罚。

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