@inproceedings{shalyminov-etal-2018-neural,
    title = "Neural Response Ranking for Social Conversation: A Data-Efficient Approach",
    author = "Shalyminov, Igor  and
      Du{\v{s}}ek, Ond{\v{r}}ej  and
      Lemon, Oliver",
    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://aclanthology.org/W18-5701",
    doi = "10.18653/v1/W18-5701",
    pages = "1--8",
    abstract = "The overall objective of {`}social{'} dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting {`}good{'} system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings {--} ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.",
}