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Effect of dialog acts on word use in polylogue

机译:对话行为对多语种中单词使用的影响

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In this work we examine the effect of dialog acts on word use, in context of the influence of interlocutors in a polylogue on each other. The basic idea of this work is the extension of the cache model and the influence model by dialog act information. The cache model covers the re-usage of words and the influence model calculates the influence of interlocutors in a polylogue on each other. Both approaches could be used to improve the word prediction accuracy in a word generative model. We start to examine the usage of dialog acts to improve our word generative model in terms of perplexity. For the usage of dialog acts, a knowledge about the future dialog act is required. Therefore, we examine how dialog act miss-prediction influences the resulting performance. Further on, we introduce a new approach to generate artificial dialog acts which guarantees the knowledge about the following dialog act. Our final experiments present the improvements in terms of perplexity using our new approach in AMI, NIST and NTT meeting corpora.
机译:在这项工作中,我们根据多语言对话中对话者彼此之间的影响,研究了对话行为对单词使用的影响。这项工作的基本思想是通过对话行为信息扩展缓存模型和影响模型。缓存模型涵盖了单词的重用,影响模型计算了多语种中对话者之间的影响。两种方法都可以用来提高单词生成模型中的单词预测准确性。我们开始研究对话行为的用法,以改善困惑的单词生成模型。为了使用对话行为,需要有关未来对话行为的知识。因此,我们研究了对话行为的错误预测如何影响最终的性能。进一步,我们引入了一种新的方法来生成人为的对话行为,从而保证了有关以下对话行为的知识。我们的最终实验使用AMI,NIST和NTT会议语料库中的新方法提出了困惑方面的改进。

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