Many NLG tasks such as summarization, dialogue response, or open domain
question answering focus primarily on a source text in order to generate a
target response. This standard approach falls short, however, when a user's
intent or context of work is not easily recoverable based solely on that source
text -- a scenario that we argue is more of the rule than the exception. In
this work, we argue that NLG systems in general should place a much higher
level of emphasis on making use of additional context, and suggest that
relevance (as used in Information Retrieval) be thought of as a crucial tool
for designing user-oriented text-generating tasks. We further discuss possible
harms and hazards around such personalization, and argue that value-sensitive
design represents a crucial path forward through these challenges.

本文主要讨论了自然语言生成在设计用户文本生成任务时如何利用上下文和相关性，并探讨了这种个性化文本生成可能带来的潜在风险和通过价值敏感设计应对这些挑战的途径。

重新关注相关性：自然语言生成中的个性化

Refocusing on Relevance: Personalization in NLG

Over the last several years, end-to-end neural conversational agents have
vastly improved in their ability to carry a chit-chat conversation with humans.
However, these models are often trained on large datasets from the internet,
and as a result, may learn undesirable behaviors from this data, such as toxic
or otherwise harmful language. Researchers must thus wrestle with the issue of
how and when to release these models. In this paper, we survey the problem
landscape for safety for end-to-end conversational AI and discuss recent and
related work. We highlight tensions between values, potential positive impact
and potential harms, and provide a framework for making decisions about whether
and how to release these models, following the tenets of value-sensitive
design. We additionally provide a suite of tools to enable researchers to make
better-informed decisions about training and releasing end-to-end
conversational AI models.

本文讨论了在训练端到端对话 AI 模型时会遇到的关于安全性的困境。提供了一个基于价值观的设计框架，以帮助研究人员在决定何时和如何发布这些模型时做出更合理的决策，并提供了一套工具来帮助他们进行更明智的选择。