相关论文: Ecological Evaluation of Persuasive Messages Using…

Emotions that somebody develops based on an argument do not only depend on the argument itself - they are also influenced by a subjective evaluation of the argument's potential impact on the self. For instance, an argument to ban plastic…

Crowdsourcing information constitutes an important aspect of human-in-the-loop learning for researchers across multiple disciplines such as AI, HCI, and social science. While using crowdsourced data for subjective tasks is not new,…

As Artificial Intelligence (AI) becomes increasingly integrated into education, university students preparing for English language tests are frequently shifting between traditional search engines like Google and large language models (LLMs)…

In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. In a conversational setting such signals are usually unavailable due to the nature of the interactions, and, instead, the evaluation often relies…

Topics models, such as LDA, are widely used in Natural Language Processing. Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the…

Interactive advertising which characterized by interactivity has become the mainstream of advertising by gradually replacing traditional one-way advertising during the new media era. This paper has obeyed the research outline below,…

Keyword extraction is a foundational task in natural language processing, underpinning countless real-world applications. One of these is contextual advertising, where keywords help predict the topical congruence between ads and their…

Employees face decisions every day - in the absence of supervision. The outcome of these decisions can be influenced by digital workplace design through the power of persuasive technology. This paper provides a structured literature review…

We employ crowdsourcing to acquire time-continuous affective annotations for movie clips, and refine noisy models trained from these crowd annotations incorporating expert information within a Multi-task Learning (MTL) framework. We propose…

Interpreting individual neurons or directions in activation space is an important topic in mechanistic interpretability. Numerous automated interpretability methods have been proposed to generate such explanations, but it remains unclear…

We now have a rich and growing set of modeling tools and algorithms for inducing linguistic structure from text that is less than fully annotated. In this paper, we discuss some of the weaknesses of our current methodology. We present a new…

We describe Quizz, a gamified crowdsourcing system that simultaneously assesses the knowledge of users and acquires new knowledge from them. Quizz operates by asking users to complete short quizzes on specific topics; as a user answers the…

Emotion recognition algorithms rely on data annotated with high quality labels. However, emotion expression and perception are inherently subjective. There is generally not a single annotation that can be unambiguously declared "correct".…

While reaching for NLP systems that maximize accuracy, other important metrics of system performance are often overlooked. Prior models are easily forgotten despite their possible suitability in settings where large computing resources are…

Modern decision making tools are based on statistical analysis of abundant data, which is often collected by querying multiple individuals. We consider data collection through crowdsourcing, where independent and self-interested agents,…

After the launch of ChatGPT v.4 there has been a global vivid discussion on the ability of this artificial intelligence powered platform and some other similar ones for the automatic production of all kinds of texts, including scientific…

Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands. The quality of urban green space has been hitherto measured by expert…

How can society understand and hold accountable complex human and algorithmic decision-making systems whose systematic errors are opaque to the public? These systems routinely make decisions on individual rights and well-being, and on…

Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most existing frameworks rely on dynamic commercial search APIs,…

This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such predictions allow both humans and…