Learning to Converse with Noisy Data: Generation with Calibration.

Mingyue Shang, Zhenxin Fu, Nanyun Peng, Yansong Feng, Dongyan Zhao, and Rui Yan, in IJCAI, 2018.

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Abstract

The availability of abundant conversational data on the Internet brought prosperity to the generation-based open domain conversation systems. In the training of the generation models, existing methods generally treat all the training data equivalently. However, the data crawled from the websites may contain many noises. Blindly training with the noisy data could harm the performance of the final generation model. In this paper, we propose a generation with calibration framework, that allows high- quality data to have more influences on the generation model and reduces the effect of noisy data. Specifically, for each instance in training set, we employ a calibration network to produce a quality score for it, then the score is used for the weighted update of the generation model parameters. Experiments show that the calibrated model outperforms baseline methods on both automatic evaluation metrics and human annotations.

Bib Entry

@inproceedings{shang2018learning,
  title = {Learning to Converse with Noisy Data: Generation with Calibration.},
  author = {Shang, Mingyue and Fu, Zhenxin and Peng, Nanyun and Feng, Yansong and Zhao, Dongyan and Yan, Rui},
  booktitle = {IJCAI},
  pages = {4338--4344},
  year = {2018}
}