As humans, we use linguistic elements referencing time, such as before or
tomorrow, to easily share past experiences and future predictions. While
temporal aspects of the language have been considered in computational
linguistics, no such exploration has been done within the field of emergent
communication. We research this gap, providing the first reported temporal
vocabulary within emergent communication literature. Our experimental analysis
shows that a different agent architecture is sufficient for the natural
emergence of temporal references, and that no additional losses are necessary.
Our readily transferable architectural insights provide the basis for the
incorporation of temporal referencing into other emergent communication
environments.

人类使用引用时间的语言元素（如昨天或明天）来轻松分享过去的经历和未来的预测。我们的研究通过在紧急通信文学领域进行首次报道的时间词汇提供了对这一差距的研究，实验分析结果表明不同的代理架构足以自然产生时间参考，并且不需要额外的损失。我们的架构见解易于传递，为将时间参考引入其他紧急通信环境提供了基础。

关于新兴交流中的时间参照

On Temporal References in Emergent Communication

Our paper focuses on making use of deep neural network models to accurately
predict the range of human emotions experienced during watching movies. In this
certain setup, there exist three clear-cut input modalities that considerably
influence the experienced emotions: visual cues derived from RGB video frames,
auditory components encompassing sounds, speech, and music, and linguistic
elements encompassing actors' dialogues. Emotions are commonly described using
a two-factor model including valence (ranging from happy to sad) and arousal
(indicating the intensity of the emotion). In this regard, a Plethora of works
have presented a multitude of models aiming to predict valence and arousal from
video content. However, non of these models contain all three modalities, with
language being consistently eliminated across all of them. In this study, we
comprehensively combine all modalities and conduct an analysis to ascertain the
importance of each in predicting valence and arousal. Making use of pre-trained
neural networks, we represent each input modality in our study. In order to
process visual input, we employ pre-trained convolutional neural networks to
recognize scenes[1], objects[2], and actions[3,4]. For audio processing, we
utilize a specialized neural network designed for handling sound-related tasks,
namely SoundNet[5]. Finally, Bidirectional Encoder Representations from
Transformers (BERT) models are used to extract linguistic features[6] in our
analysis. We report results on the COGNIMUSE dataset[7], where our proposed
model outperforms the current state-of-the-art approaches. Surprisingly, our
findings reveal that language significantly influences the experienced arousal,
while sound emerges as the primary determinant for predicting valence. In
contrast, the visual modality exhibits the least impact among all modalities in
predicting emotions.

本文研究使用深度神经网络模型准确预测电影观众观看电影时所体验到的人类情感，其中包括来自 RGB 视频帧的视觉提示，涵盖声音、语音和音乐的听觉组件，以及包含演员对话的语言元素。

使用深度神经网络增强电影情感体验预测：音频和语言的重要性

Enhancing the Prediction of Emotional Experience in Movies using Deep  Neural Networks: The Significance of Audio and Language

Natural language processing researchers develop models of grammar, meaning
and human communication based on written text. Due to task and data
differences, what is considered text can vary substantially across studies. A
conceptual framework for systematically capturing these differences is lacking.
We argue that clarity on the notion of text is crucial for reproducible and
generalizable NLP. Towards that goal, we propose common terminology to discuss
the production and transformation of textual data, and introduce a two-tier
taxonomy of linguistic and non-linguistic elements that are available in
textual sources and can be used in NLP modeling. We apply this taxonomy to
survey existing work that extends the notion of text beyond the conservative
language-centered view. We outline key desiderata and challenges of the
emerging inclusive approach to text in NLP, and suggest systematic
community-level reporting as a crucial next step to consolidate the discussion.

本研究旨在确定自然语言处理中所谓文本的概念，并提出一套通用术语和分类体系，以扩展文本的范围，从而更好地进行 NLP 建模。该研究强调了包容性方法在 NLP 中处理文本的重要性，并呼吁开发者采用系统化的报告方式，以巩固该话题。