It is often difficult to correctly infer a writer's emotion from text
exchanged online, and differences in recognition between writers and readers
can be problematic. In this paper, we propose a new framework for detecting
sentences that create differences in emotion recognition between the writer and
the reader and for detecting the kinds of expressions that cause such
differences. The proposed framework consists of a bidirectional encoder
representations from transformers (BERT)-based detector that detects sentences
causing differences in emotion recognition and an analysis that acquires
expressions that characteristically appear in such sentences. The detector,
based on a Japanese SNS-document dataset with emotion labels annotated by both
the writer and three readers of the social networking service (SNS) documents,
detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise
to differences in the recognition of anger. Because SNS documents contain many
sentences whose meaning is extremely difficult to interpret, by analyzing the
sentences detected by this detector, we obtained several expressions that
appear characteristically in hidden-anger sentences. The detected sentences and
expressions do not convey anger explicitly, and it is difficult to infer the
writer's anger, but if the implicit anger is pointed out, it becomes possible
to guess why the writer is angry. Put into practical use, this framework would
likely have the ability to mitigate problems based on misunderstandings.

本文提出一种基于 BERT 的检测框架，能够检测出导致作者和读者之间情感识别差异的句子，并分析出这些句子所具有的表达特征。采用日语 SNS 文档数据集，该检测器探测出了 AUC=0.772 的 'hidden-anger sentences'。在分析表达特征后，如果隐含的愤怒被指出，那么就可能猜测出作家的情感状态，从而有效地解决由误解引起的问题。

社交网络服务文档中引起情感识别差异的表达方式

Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents

We present a single-image 3D face synthesis technique that can handle
challenging facial expressions while recovering fine geometric details. Our
technique employs expression analysis for proxy face geometry generation and
combines supervised and unsupervised learning for facial detail synthesis. On
proxy generation, we conduct emotion prediction to determine a new
expression-informed proxy. On detail synthesis, we present a Deep Facial Detail
Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs
both geometry and appearance loss functions. For geometry, we capture 366
high-quality 3D scans from 122 different subjects under 3 facial expressions.
For appearance, we use additional 20K in-the-wild face images and apply
image-based rendering to accommodate lighting variations. Comprehensive
experiments demonstrate that our framework can produce high-quality 3D faces
with realistic details under challenging facial expressions.

通过表情分析生成满足要求的脸部几何表达形式并结合有监督和无监督学习实现面部细节合成的单图像三维面部综合技术.