Zixuan Li


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Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
Long Bai | Saiping Guan | Jiafeng Guo | Zixuan Li | Xiaolong Jin | Xueqi Cheng
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Scripts are structured sequences of events together with the participants, which are extracted from the texts. Script event prediction aims to predict the subsequent event given the historical events in the script. Two kinds of information facilitate this task, namely, the event-level information and the script-level information. At the event level, existing studies view an event as a verb with its participants, while neglecting other useful properties, such as the state of the participants. At the script level, most existing studies only consider a single event sequence corresponding to one common protagonist. In this paper, we propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction. At the event level, MCPredictor utilizes the rich information in the text to obtain more comprehensive event semantic representations. At the script-level, it considers multiple event sequences corresponding to different participants of the subsequent event. The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.

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Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning
Zhongni Hou | Xiaolong Jin | Zixuan Li | Long Bai
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Zixuan Li | Xiaolong Jin | Saiping Guan | Wei Li | Jiafeng Guo | Yuanzhuo Wang | Xueqi Cheng
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.