36 citations · 39 across the 4 of their papers we have counts for
6 papers
Inconsistencies in Masked Language Models
Tom Young, Yunan Chen, Yang You
Learning to predict masked tokens in a sequence has been shown to be a helpful pretraining objective for powerful language models such as PaLM2. After training, such masked languag…
From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems
Tom Young
The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well…
Fusing task-oriented and open-domain dialogues in conversational agents
Tom Young, Frank Xing, Vlad Pandelea +2
The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform goal-oriented functio…
Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey
Jinjie Ni, Tom Young, Vlad Pandelea +2
Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving stu…
Augmenting End-to-End Dialog Systems with Commonsense Knowledge
Tom Young, Erik Cambria, Iti Chaturvedi +3
Building dialog agents that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where…
Recent Trends in Deep Learning Based Natural Language Processing
Tom Young, Devamanyu Hazarika, Soujanya Poria +1
Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variet…