Search Results for author:
Found 11 papers, 4 papers with code
We’ve had this conversation before: A Novel Approach to Measuring Dialog Similarity
The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.
SNAP: Semantic Stories for Next Activity Prediction
To address this gap, we propose the novel SNAP method that leverages language foundation models by constructing semantic contextual stories from the process historical event logs and using them for the next activity prediction.
Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition
In this study, we examine the potential of a virtual AI teacher in emulating the techniques of human educators for motor skill acquisition.
Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges
This paper explores these new challenges and opportunities, analyzes the main aspects of trust in AI agents discussed in existing literature, and identifies specific considerations and challenges relevant to this new generation of automation agents.
Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration
Using this dataset, we demonstrate crowd-wisdom and goal-driven approaches to prescriptive process monitoring.
Understanding the Properties of Generated Corpora
Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora.
We've had this conversation before: A Novel Approach to Measuring Dialog Similarity
The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.
Phase Transitions in Kyle's Model with Market Maker Profit Incentives
In addition to the classical framework, a revenue term is added to the market maker's performance function, which is proportional to the order flow and to the size of the bid-ask spread.
Not Enough Data? Deep Learning to the Rescue!
Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks.
Deep Dominance - How to Properly Compare Deep Neural Models
Comparing between Deep Neural Network (DNN) models based on their performance on unseen data is crucial for the progress of the NLP field.
The Hitchhiker's Guide to Testing Statistical Significance in Natural Language Processing
We establish the fundamental concepts of significance testing and discuss the specific aspects of NLP tasks, experimental setups and evaluation measures that affect the choice of significance tests in NLP research.
