Daisy Zhe Wang

Also published as: Zhe Wang


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TutorialVQA: Question Answering Dataset for Tutorial Videos
Anthony Colas | Seokhwan Kim | Franck Dernoncourt | Siddhesh Gupte | Zhe Wang | Doo Soon Kim
Proceedings of the 12th Language Resources and Evaluation Conference

Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.


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Consensus Maximization Fusion of Probabilistic Information Extractors
Miguel Rodríguez | Sean Goldberg | Daisy Zhe Wang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Chinese Poetry Generation with Planning based Neural Network
Zhe Wang | Wei He | Hua Wu | Haiyang Wu | Wei Li | Haifeng Wang | Enhong Chen
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Chinese poetry generation is a very challenging task in natural language processing. In this paper, we propose a novel two-stage poetry generating method which first plans the sub-topics of the poem according to the user’s writing intent, and then generates each line of the poem sequentially, using a modified recurrent neural network encoder-decoder framework. The proposed planning-based method can ensure that the generated poem is coherent and semantically consistent with the user’s intent. A comprehensive evaluation with human judgments demonstrates that our proposed approach outperforms the state-of-the-art poetry generating methods and the poem quality is somehow comparable to human poets.


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Automatic Knowledge Base Construction using Probabilistic Extraction, Deductive Reasoning, and Human Feedback
Daisy Zhe Wang | Yang Chen | Sean Goldberg | Christan Grant | Kun Li
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)