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Linzhang Mo

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2 papers

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AAAI Conference 2021 Conference Paper

Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing

Qingbao Huang
Mingyi Fu
Linzhang Mo
Yi Cai
Jingyun Xu
Pijian Li
Qing Li
Ho-fung Leung

Question generation is a challenging task and has attracted widespread attention in recent years. Although previous studies have made great progress, there are still two main shortcomings: First, previous work did not simultaneously capture the sequence information and structure information hidden in the context, which results in poor results of the generated questions. Second, the generated questions cannot be answered by the given context. To tackle these issues, we propose an entity guided question generation model with contextual structure information and sequence information capturing. We use a Graph Convolutional Network and a Bidirectional Long Short Term Memory Network to capture the structure information and sequence information of the context, simultaneously. In addition, to improve the answerability of the generated questions, we use an entity-guided approach to obtain question type from the answer, and jointly encode the answer and question type. Both automatic and manual metrics show that our model can generate comparable questions with state-of-the-art models. Our code is available at https: //github. com/VISLANG-Lab/EGSS.

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AAAI Conference 2021 Conference Paper

Story Ending Generation with Multi-Level Graph Convolutional Networks over Dependency Trees

Qingbao Huang
Linzhang Mo
Pijian Li
Yi Cai
Qingguang Liu
Jielong Wei
Qing Li
Ho-fung Leung

As an interesting and challenging task, story ending generation aims at generating a reasonable and coherent ending for a given story context. The key challenge of the task is to comprehend the context sufficiently and capture the hidden logic information effectively, which has not been well explored by most existing generative models. To tackle this issue, we propose a context-aware Multi-level Graph Convolutional Networks over Dependency Parse (MGCN-DP) trees to capture dependency relations and context clues more effectively. We utilize dependency parse trees to facilitate capturing relations and events in the context implicitly, and Multilevel Graph Convolutional Networks to update and deliver the representation crossing levels to obtain richer contextual information. Both automatic and manual evaluations show that our MGCN-DP can achieve comparable performance with state-of-the-art models. Our source code is available at https: //github. com/VISLANG-Lab/MLGCN-DP.

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