@inproceedings{du-black-2018-data,
    title = "Data Augmentation for Neural Online Chats Response Selection",
    author = "Du, Wenchao  and
      Black, Alan",
    editor = "Chuklin, Aleksandr  and
      Dalton, Jeff  and
      Kiseleva, Julia  and
      Borisov, Alexey  and
      Burtsev, Mikhail",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-5708/",
    doi = "10.18653/v1/W18-5708",
    pages = "52--58",
    abstract = "Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models. We investigate two data augmentation proxies, permutation and flipping, for neural dialog response selection task on various models over multiple datasets, including both Chinese and English languages. Different from standard data augmentation techniques, our method combines the original and synthesized data for prediction. Empirical results show that our approach can gain 1 to 3 recall-at-1 points over baseline models in both full-scale and small-scale settings."
}