资源分类
》 资源 》论文》Pretraining Methods for Dialog Context Representation Learning

Pretraining Methods for Dialog Context Representation Learning

2019-09-23 | |  122 |   55 |   0 0 0
Abstract This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using the MultiWoz dataset and strong performance improvement is observed. Further evaluation shows that our pretraining objectives result in not only better performance, but also better convergence, models that are less data hungry and have better domain generalizability

上一篇：Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good

下一篇：Proactive Human-Machine Conversation with Explicit Conversation Goals

用户评价
全部评价

热门资源

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Rating-Boosted La...

    The performance of a recommendation system reli...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...