Task-Oriented dialogue systems use four connected modules such as Natural Language Understanding (NLU), Dialogue State Tracker (DST), Dialogue Policy (DP) and Natural Language Generator (NLG). A research challenge is to learn each module with the least amount of samples (i.e., few-shots) given the high cost related to the data collection. The most common and effective technique to solve this problem is transferring learning, where large language models, either pre-trained on text or task-specific data, are fine-tuned on the few samples. These methods require fine-tuning steps and a set of parameters for each task. Differently, language models such as GPT-2 (Radford et al., 2019) and GPT-3 Brown et al., 2020) allows few-shot learning by priming the model with few-examples. In this paper, we evaluate the few-shot ability of Language Models such as GPT-2 by priming in the NLU, DST, DP and NLG tasks. Importantly, we highlight the current limitations of this approach and we discuss the possible implication to future work.

本文探讨自然语言理解、对话状态跟踪、对话策略和自然语言生成这四个模块在少数据样本下学习的挑战，并评估了使用自然语言生成模型进行少样本迁移学习的效果和局限性，为未来研究提供了启示。

以语言模型为少样本学习者的任务导向式对话系统