Improving Unsegmented Dialogue Turns Annotation with N-gram Transducers

@inproceedings{MartnezHinarejos2009ImprovingUD,
  title={Improving Unsegmented Dialogue Turns Annotation with N-gram Transducers},
  author={Carlos David Mart{\'i}nez-Hinarejos and Vicent Tamarit and Jos{\'e}-Miguel Bened{\'i}},
  booktitle={PACLIC},
  year={2009}
}
The statistical models used for dialogue systems need annotated data (dialogues) to infer their statistical parameters. Dialogues are usually annotated in terms of Dialogue Acts (DA). The annotation problem can be attacked with statistical models, that avoid annotating the dialogues from scratch. Most previous works on automatic statistical annotation assume that the dialogue turns are segmented into the corresponding meaningful units. However, this segmentation is not usually available. Most… CONTINUE READING

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