@inproceedings{srivastava-etal-2021-pretrain,
    title = "Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting",
    author = "Srivastava, Manisha  and
      Lu, Yichao  and
      Peschon, Riley  and
      Li, Chenyang",
    editor = "Kim, Young-bum  and
      Li, Yunyao  and
      Rambow, Owen",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.naacl-industry.5/",
    doi = "10.18653/v1/2021.naacl-industry.5",
    pages = "34--40",
    abstract = "One main challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. In this work, we present a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset. We show that pretraining using unlabeled data can bring better model performance with a 31{\%} boost in Recall@1 compared with no pretraining. The proposed finetuning technique based on a small amount of high-quality, annotated data resulted in 26{\%} offline and 33{\%} online performance improvement in Recall@1 over the pretrained model. The model is deployed in an agent-support application and evaluated on live customer service contacts, providing additional insights into the real-world implications compared with most other publications in the domain often using asynchronous transcripts (e.g. Reddit data). The high performance of 74{\%} Recall@1 shown in the customer service example demonstrates the effectiveness of this pretrain-finetune approach in dealing with the limited supervised data challenge."
}