Search Results for author:
Found 28 papers, 5 papers with code
Lifelong Explainer for Lifelong Learners
Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer.
Curriculum Learning Effectively Improves Low Data VQA
In this paper, we propose curriculum-based learning (CL) regime to increase the accuracy of VQA models trained on small datasets.
Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations
We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information.
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations
While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is a crucial ability in human communication.
SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations
We have released our code and software at:~\url{https://github. com/AnonymousEACLDemo/SADAS}.
Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (e. g., vehicle, laptop), have been gaining research interest in recent years.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
Learning to Explain: Generating Stable Explanations Fast
The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged.
BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning
In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty.
Influence of Time and Risk on Response Acceptability in a Simple Spoken Dialogue System
We describe a longitudinal user study conducted in the context of a Spoken Dialogue System for a household robot, where we examined the influence of time displacement and situational risk on users{'} preferred responses.
Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i. e. human transcriptions, instead of Automatic Speech Recognition (ASR)'s transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
The Context-Dependent Additive Recurrent Neural Net
Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP).
Preserving Distributional Information in Dialogue Act Classification
This paper introduces a novel training/decoding strategy for sequence labeling.
A Generative Attentional Neural Network Model for Dialogue Act Classification
We propose a novel generative neural network architecture for Dialogue Act classification.
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts
We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs).
A Corpus of Tables in Full-Text Biomedical Research Publications
The high inter-annotator agreement achieved on the corpus, and the generic nature of our annotation approach, suggest that the developed guidelines can serve as a general framework for table annotation in biomedical and other scientific domains.
Strategies for Generating Micro Explanations for Bayesian Belief Networks
Bayesian Belief Networks have been largely overlooked by Expert Systems practitioners on the grounds that they do not correspond to the human inference mechanism.
