Integrating External Event Knowledge for Script Learning

Script learning aims to predict the subsequent event according to the existing event chain. Recent studies focus on event co-occurrence to solve this problem. However, few studies integrate external event knowledge to solve this problem. With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event. In this work, we integrate event knowledge from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event. We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage. In the knowledge retrieval stage, we select relevant external event knowledge from ASER. In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers. Experiments on the widely-used MultiChoice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.

[1]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[2]  Nathanael Chambers,et al.  Unsupervised Learning of Narrative Event Chains , 2008, ACL.

[3]  Raymond J. Mooney,et al.  Learning Statistical Scripts with LSTM Recurrent Neural Networks , 2016, AAAI.

[4]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[5]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[6]  Songlin Hu,et al.  SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next , 2019, AAAI.

[7]  Dan Goldwasser,et al.  Multi-Relational Script Learning for Discourse Relations , 2019, ACL.

[8]  Yue Zhang,et al.  Integrating Order Information and Event Relation for Script Event Prediction , 2017, EMNLP.

[9]  Ting Liu,et al.  Event Representation Learning Enhanced with External Commonsense Knowledge , 2019, EMNLP/IJCNLP.

[10]  Xin Liu,et al.  ASER: A Large-scale Eventuality Knowledge Graph , 2019, WWW.

[11]  Stephen Clark,et al.  What Happens Next? Event Prediction Using a Compositional Neural Network Model , 2016, AAAI.

[12]  Marie-Francine Moens,et al.  Skip N-grams and Ranking Functions for Predicting Script Events , 2012, EACL.

[13]  Ting Liu,et al.  Constructing Narrative Event Evolutionary Graph for Script Event Prediction , 2018, IJCAI.

[14]  Roger C. Schank,et al.  Scripts, plans, goals and understanding: an inquiry into human knowledge structures , 1978 .

[15]  Raymond J. Mooney,et al.  Statistical Script Learning with Multi-Argument Events , 2014, EACL.