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Discriminative Sentence Modeling for Story Ending Prediction
Yiming Cui, Wanxiang Che, Wei-Nan Zhang +3
Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense…
Contextual Recurrent Units for Cloze-style Reading Comprehension
Yiming Cui, Wei-Nan Zhang, Wanxiang Che +4
Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks. In this pape…
Improving Machine Reading Comprehension via Adversarial Training
Ziqing Yang, Yiming Cui, Wanxiang Che +3
Adversarial training (AT) as a regularization method has proved its effectiveness in various tasks, such as image classification and text classification. Though there are successfu…
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever
Libo Qin, Yijia Liu, Wanxiang Che +3
Querying the knowledge base (KB) has long been a challenge in the end-to-end task-oriented dialogue system. Previous sequence-to-sequence (Seq2Seq) dialogue generation work treats…
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing
Yuxuan Wang, Wanxiang Che, Jiang Guo +2
This paper investigates the problem of learning cross-lingual representations in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient app…
A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
Yutai Hou, Meng Fang, Wanxiang Che +1
Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotate…