论文标题
Godel：针对目标导向对话的大规模预训练
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog
论文作者
论文摘要
我们介绍了Godel（接地开放对话语言模型），这是对话框的大型预训练的语言模型。与诸如Dialogpt之类的早期模型相反，Godel利用了一个新的扎根预训练阶段，旨在更好地支持将Godel调整为广泛的下游对话框任务，这些任务需要当前对话外部的信息（例如，数据库或文档）来产生良好的响应。针对一系列基准测试的实验，这些基准包括以任务为导向的对话框，对话质量质量质量图和接地的开放域对话框，表明，在人类和自动评估方面，Godel在几次射击的微调设置中都超过了最先进的预训练的对话模型。我们评估方法的一个新颖特征是引入了效用概念，该概念除了其交流特征（内在评估）外，还评估了响应的有用性（外部评估）。我们表明，外部评估提供了改进的通道间一致性和与自动指标的相关性。代码和数据处理脚本公开可用。
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information external to the current conversation (e.g., a database or document) to produce good responses. Experiments against an array of benchmarks that encompass task-oriented dialog, conversational QA, and grounded open-domain dialog show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot fine-tuning setups, in terms of both human and automatic evaluation. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses (extrinsic evaluation) in addition to their communicative features (intrinsic evaluation). We show that extrinsic evaluation offers improved inter-annotator agreement and correlation with automated metrics. Code and data processing scripts are publicly available.
