Attitude is omnipresent in almost every type of text. There has yet to be any
relevant research on attitudinal shifts in self-translation. The Chinese
version of Between Tears and Laughter is a rare case of self-translation and
co-translation in that the first 11 chapters are self-translated by Lin Yutang,
and the last 12 chapters by Xu Chengbin. The current study conducted a word
frequency analysis of this book's English and Chinese versions with LIWC and
AntConc, and made comparative research into Lin Yutang's attitudinal changes.
The results show that due to different writing purposes and readerships, there
is less anger in Lin's self-translation (M=0.7755, SD=0.2775) than in the first
11 chapters of the English original (M=1.1036, SD=0.3861), which is a
significant difference (t=2.2892, p=0.0331). This attitudinal change is also
reflected in the translations of some n-grams containing anger words. In
contrast, there is no significant difference (t=1.88, p=0.07) between Xu's
co-translation and the corresponding part of the original in attitude "anger".
This paper believes that corpus tools can help co-translators keep their
translation consistent in attitude.

通过使用 LIWC 和 AntConc 进行词频分析和比较研究，本研究发现由于写作目的和读者群体的不同，林语堂在自我翻译的版本中的表达更少地带有愤怒情绪，相比之下中国原版中的前 11 章的英文原版有着显著的愤怒情绪，并认为与语料库工具的使用能使合作翻译维持内容一致性及态度一致性.

林语堂《笑傲江湖》自译中态度变化的基于语料库的分析

A Corpus-based Analysis of Attitudinal Changes in Lin Yutang's  Self-translation of Between Tears and Laughter

Stance detection infers a text author's attitude towards a target. This is
challenging when the model lacks background knowledge about the target. Here,
we show how background knowledge from Wikipedia can help enhance the
performance on stance detection. We introduce Wikipedia Stance Detection BERT
(WS-BERT) that infuses the knowledge into stance encoding. Extensive results on
three benchmark datasets covering social media discussions and online debates
indicate that our model significantly outperforms the state-of-the-art methods
on target-specific stance detection, cross-target stance detection, and
zero/few-shot stance detection.

本文介绍了一种利用维基百科知识的立场检测算法 WS-BERT，通过将知识注入到立场编码中增强了算法的表现，在跨目标、少样本等多个数据集上性能显著优于现有方法。

从维基百科注入知识以增强立场检测

Infusing Knowledge from Wikipedia to Enhance Stance Detection

As AI-enhanced technologies become common in a variety of domains, there is
an increasing need to define and examine the trust that users have in such
technologies. Given the progress in the development of AI, a correspondingly
sophisticated understanding of trust in the technology is required. This paper
addresses this need by explaining the role of trust on the intention to use AI
technologies. Study 1 examined the role of trust in the use of AI voice
assistants based on survey responses from college students. A path analysis
confirmed that trust had a significant effect on the intention to use AI, which
operated through perceived usefulness and participants' attitude toward voice
assistants. In study 2, using data from a representative sample of the U.S.
population, different dimensions of trust were examined using exploratory
factor analysis, which yielded two dimensions: human-like trust and
functionality trust. The results of the path analyses from Study 1 were
replicated in Study 2, confirming the indirect effect of trust and the effects
of perceived usefulness, ease of use, and attitude on intention to use.
Further, both dimensions of trust shared a similar pattern of effects within
the model, with functionality-related trust exhibiting a greater total impact
on usage intention than human-like trust. Overall, the role of trust in the
acceptance of AI technologies was significant across both studies. This
research contributes to the advancement and application of the TAM in
AI-related applications and offers a multidimensional measure of trust that can
be utilized in the future study of trustworthy AI.

本论文探讨了信任对使用人工智能技术意图的影响，结果显示信任通过感知有用性和使用者对助手的态度影响了使用意图，功能相关的信任对使用意图的影响大于人类的信任，在 AI 相关应用中，信任对技术的接受起着重要的作用，并提供了可用于未来研究的基于多维度的信任度量方法。