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.

中文翻译：

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重新关注相关性：NLG 中的个性化

许多 NLG 任务，例如摘要、对话响应或开放域问答，主要关注源文本以生成目标响应。然而，当用户的意图或工作上下文无法仅根据该源文本轻松恢复时，这种标准方法就存在不足——我们认为这种情况更像是规则而不是例外。在这项工作中，我们认为 NLG 系统一般应该更加重视利用额外的上下文，并建议将相关性（如信息检索中使用的）视为设计面向用户的文本的关键工具- 生成任务。我们进一步讨论了围绕这种个性化可能带来的危害和危害，并认为价值敏感的设计代表了克服这些挑战的关键途径。