Recent work building open-domain chatbots has demonstrated that increasing
model size improves performance. On the other hand, latency and connectivity
considerations dictate the move of digital assistants on the device. Giving a
digital assistant like Siri, Alexa, or Google Assistant the ability to discuss
just about anything leads to the need for reducing the chatbot model size such
that it fits on the user's device. We demonstrate that low parameter models can
simultaneously retain their general knowledge conversational abilities while
improving in a specific domain. Additionally, we propose a generic framework
that accounts for variety in question types, tracks reference throughout
multi-turn conversations, and removes inconsistent and potentially toxic
responses. Our framework seamlessly transitions between chatting and performing
transactional tasks, which will ultimately make interactions with digital
assistants more human-like. We evaluate our framework on 1 internal and 4
public benchmark datasets using both automatic (Perplexity) and human (SSA -
Sensibleness and Specificity Average) evaluation metrics and establish
comparable performance while reducing model parameters by 90%.

我们提出了一种通用的框架，该框架可以在保持低参数的前提下，同时保持聊天机器人的通用知识和特定领域的能力，并可以在交互中实现无缝转换，并通过对话进行参考跟踪和去除不一致的回应来改善数字助手的交互效果。

低资源开放领域生成式聊天机器人的适应

Low-Resource Adaptation of Open-Domain Generative Chatbots

We introduce a curriculum learning approach to adapt generic neural machine
translation models to a specific domain. Samples are grouped by their
similarities to the domain of interest and each group is fed to the training
algorithm with a particular schedule. This approach is simple to implement on
top of any neural framework or architecture, and consistently outperforms both
unadapted and adapted baselines in experiments with two distinct domains and
two language pairs.

介绍了一种课程学习方法，将通用的神经机器翻译模型适应于特定领域，实验结果表明，在两个领域和两种语言对中，该方法始终优于基准模型。