Ziwei Ji


2022

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VScript: Controllable Script Generation with Visual Presentation
Ziwei Ji | Yan Xu | I-Tsun Cheng | Samuel Cahyawijaya | Rita Frieske | Etsuko Ishii | Min Zeng | Andrea Madotto | Pascale Fung
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations

In order to offer a customized script tool and inspire professional scriptwriters, we present VScript. It is a controllable pipeline that generates complete scripts, including dialogues and scene descriptions, as well as presents visually using video retrieval. With an interactive interface, our system allows users to select genres and input starting words that control the theme and development of the generated script. We adopt a hierarchical structure, which first generates the plot, then the script and its visual presentation. A novel approach is also introduced to plot-guided dialogue generation by treating it as an inverse dialogue summarization. The experiment results show that our approach outperforms the baselines on both automatic and human evaluations, especially in genre control.

2020

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Multi-hop Question Generation with Graph Convolutional Network
Dan Su | Yan Xu | Wenliang Dai | Ziwei Ji | Tiezheng Yu | Pascale Fung
Findings of the Association for Computational Linguistics: EMNLP 2020

Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. on. The code is publicly availableat https://github.com/HLTCHKU