Automatic personality trait assessment is essential for high-quality
human-machine interactions. Systems capable of human behavior analysis could be
used for self-driving cars, medical research, and surveillance, among many
others. We present a multimodal deep neural network with a Siamese extension
for apparent personality trait prediction trained on short video recordings and
exploiting modality invariant embeddings. Acoustic, visual, and textual
information are utilized to reach high-performance solutions in this task. Due
to the highly centralized target distribution of the analyzed dataset, the
changes in the third digit are relevant. Our proposed method addresses the
challenge of under-represented extreme values, achieves 0.0033 MAE average
improvement, and shows a clear advantage over the baseline multimodal DNN
without the introduced module.

用深度神经网络进行多模态的自动个性化特征评估，以提高人机交互的质量。

通过跨模态嵌入增强明显人格特征分析

Enhancing Apparent Personality Trait Analysis with Cross-Modal  Embeddings

In the recent shift towards human-centric AI, the need for machines to
accurately use natural language has become increasingly important. While a
common approach to achieve this is to train large language models, this method
presents a form of learning misalignment where the model may not capture the
underlying structure and reasoning humans employ in using natural language,
potentially leading to unexpected or unreliable behavior. Emergent
communication (Emecom) is a field of research that has seen a growing number of
publications in recent years, aiming to develop artificial agents capable of
using natural language in a way that goes beyond simple discriminative tasks
and can effectively communicate and learn new concepts. In this review, we
present Emecom under two aspects. Firstly, we delineate all the common
proprieties we find across the literature and how they relate to human
interactions. Secondly, we identify two subcategories and highlight their
characteristics and open challenges. We encourage researchers to work together
by demonstrating that different methods can be viewed as diverse solutions to a
common problem and emphasize the importance of including diverse perspectives
and expertise in the field. We believe a deeper understanding of human
communication is crucial to developing machines that can accurately use natural
language in human-machine interactions.

发展人机互动中准确使用自然语言的机器，重要性愈发突显。本文回顾了近年来涉及新兴交流（Emergent communication）的文献，分析了其公共特性、两个子类别和挑战，并强调了多元视角和专业知识在领域中的重要性，主张研究者们共同努力以寻找共同问题的多种解决方案，加深对人类交流的理解，从而发展出更能准确使用自然语言的机器。

走向更贴近人类的 AI 交流：紧急交流研究综述

Towards More Human-like AI Communication: A Review of Emergent  Communication Research

We study open domain dialogue generation with dialogue acts designed to
explain how people engage in social chat. To imitate human behavior, we propose
managing the flow of human-machine interactions with the dialogue acts as
policies. The policies and response generation are jointly learned from
human-human conversations, and the former is further optimized with a
reinforcement learning approach. With the dialogue acts, we achieve significant
improvement over state-of-the-art methods on response quality for given
contexts and dialogue length in both machine-machine simulation and
human-machine conversation.

本文研究采用对话行为来模拟人类社交聊天的开放领域对话生成，利用行为策略来管理人机交互流程，并结合强化学习方法对策略进行优化，从而在机器仿真和人机交互中获得了显著响应质量的提升。