Although the NLP community has adopted central differential privacy as a
go-to framework for privacy-preserving model training or data sharing, the
choice and interpretation of the key parameter, privacy budget $\varepsilon$
that governs the strength of privacy protection, remains largely arbitrary. We
argue that determining the $\varepsilon$ value should not be solely in the
hands of researchers or system developers, but must also take into account the
actual people who share their potentially sensitive data. In other words: Would
you share your instant messages for $\varepsilon$ of 10? We address this
research gap by designing, implementing, and conducting a behavioral experiment
(311 lay participants) to study the behavior of people in uncertain
decision-making situations with respect to privacy-threatening situations.
Framing the risk perception in terms of two realistic NLP scenarios and using a
vignette behavioral study help us determine what $\varepsilon$ thresholds would
lead lay people to be willing to share sensitive textual data - to our
knowledge, the first study of its kind.

利用行为研究实验，研究确定实际参与者愿意共享的 epsilon 阈值，以确定涉及敏感文本数据的两个实际 NLP 场景的人们的行为。

分享与否：普通民众愿意为了提供差分隐私自然语言处理系统而接受哪些风险？

To share or not to share: What risks would laypeople accept to give  sensitive data to differentially-private NLP systems?

Current computational models capturing words' meaning mostly rely on textual
corpora. While these approaches have been successful over the last decades,
their lack of grounding in the real world is still an ongoing problem. In this
paper, we focus on visual grounding of word embeddings and target two important
questions. First, how can language benefit from vision in the process of visual
grounding? And second, is there a link between visual grounding and abstract
concepts? We investigate these questions by proposing a simple yet effective
approach where language benefits from vision specifically with respect to the
modeling of both concrete and abstract words. Our model aligns word embeddings
with their corresponding visual representation without deteriorating the
knowledge captured by textual distributional information. We apply our model to
a behavioral experiment reported by Günther et al. (2020), which addresses
the plausibility of having visual mental representations for abstract words.
Our evaluation results show that: (1) It is possible to predict human behaviour
to a large degree using purely textual embeddings. (2) Our grounded embeddings
model human behavior better compared to their textual counterparts. (3)
Abstract concepts benefit from visual grounding implicitly through their
connections to concrete concepts, rather than from having corresponding visual
representations.

本文探讨语言与视觉的互动方式对视觉基础单词嵌入的影响，并使用 Günther 等人 (2020) 的行为实验说明了基于视觉图像建模的嵌入模型在人类的行为表现上更佳。同时发现抽象概念从视觉连接的角度受到视觉基础单词的间接影响。