Researcher profile

Tom Young

5 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Recent Trends in Deep Learning Based Natural Language Processing

    2017 · arXiv (Cornell University)

    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 variety of model designs and methods have blossomed in the context of …

  2. Commonsense Knowledge Aware Conversation Generation with Graph Attention

    2018

    Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a …

  3. Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

    2018 · IEEE Computational Intelligence Magazine

    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 variety of model designs and methods have blossomed in the context of …

  4. Augmenting End-to-End Dialogue Systems With Commonsense Knowledge

    2018 · Proceedings of the AAAI Conference on Artificial Intelligence

    Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an …

  5. Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents

    2022 · Proceedings of the AAAI Conference on Artificial Intelligence

    The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform task-specific functions, and open-domain dialogue (ODD) systems, which focus on non-goal-oriented chitchat. The two …