In social settings, much of human behavior is governed by unspoken rules of
conduct. For artificial systems to be fully integrated into social
environments, adherence to such norms is a central prerequisite. We investigate
whether contemporary NLG models can function as behavioral priors for systems
deployed in social settings by generating action hypotheses that achieve
predefined goals under moral constraints. Moreover, we examine if models can
anticipate likely consequences of (im)moral actions, or explain why certain
actions are preferable by generating relevant norms. For this purpose, we
introduce 'Moral Stories', a crowd-sourced dataset of structured, branching
narratives for the study of grounded, goal-oriented social reasoning. Finally,
we propose decoding strategies that effectively combine multiple expert models
to significantly improve the quality of generated actions, consequences, and
norms compared to strong baselines, e.g. though abductive reasoning.

探究自然语言生成模型作为行为先验条件用于社交环境中行为的假设生成，结合 Moral Stories 数据集研究并提出解码策略，通过综合专家模型实现优质行为生成、后果和规范生成。

道德故事：关于规范、意图、行动及其后果的情境推理

Moral Stories: Situated Reasoning about Norms, Intents, Actions, and  their Consequences

Intimacy is a fundamental aspect of how we relate to others in social
settings. Language encodes the social information of intimacy through both
topics and other more subtle cues (such as linguistic hedging and swearing).
Here, we introduce a new computational framework for studying expressions of
the intimacy in language with an accompanying dataset and deep learning model
for accurately predicting the intimacy level of questions (Pearson's r=0.87).
Through analyzing a dataset of 80.5M questions across social media, books, and
films, we show that individuals employ interpersonal pragmatic moves in their
language to align their intimacy with social settings. Then, in three studies,
we further demonstrate how individuals modulate their intimacy to match social
norms around gender, social distance, and audience, each validating key
findings from studies in social psychology. Our work demonstrates that intimacy
is a pervasive and impactful social dimension of language.

本研究使用一种新的计算框架研究了人们在语言中表达亲密度的方式，并开发了相应的数据集和深度学习模型，用于准确预测问题的亲密水平，并通过三个研究证明了这种表达方式与社会规范有关。