@inproceedings{towle-zhou-2023-end,
    title = "End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems",
    author = "Towle, Benjamin  and
      Zhou, Ke",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.findings-emnlp.510/",
    doi = "10.18653/v1/2023.findings-emnlp.510",
    pages = "7610--7622",
    abstract = "Reply suggestion systems represent a staple component of many instant messaging and email systems. However, the requirement to produce sets of replies, rather than individual replies, makes the task poorly suited for out-of-the-box retrieval architectures, which only consider individual message-reply similarity. As a result, these system often rely on additional post-processing modules to diversify the outputs. However, these approaches are ultimately bottlenecked by the performance of the initial retriever, which in practice struggles to present a sufficiently diverse range of options to the downstream diversification module, leading to the suggestions being less relevant to the user. In this paper, we consider a novel approach that radically simplifies this pipeline through an autoregressive text-to-text retrieval model, that learns the smart reply task end-to-end from a dataset of (message, reply set) pairs obtained via bootstrapping. Empirical results show this method consistently outperforms a range of state-of-the-art baselines across three datasets, corresponding to a 5.1{\%}-17.9{\%} improvement in relevance, and a 0.5{\%}-63.1{\%} improvement in diversity compared to the best baseline approach. We make our code publicly available."
}