Abstract
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 Multi- Choice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.
Highlights
A script is a sequence of stereotypical events related to a protagonist
We show that our method can achieve state-of-the-art performance on the Multi-Choice Narrative Cloze (MCNC) task
We can see that our method can achieve state-of-the-art performance and obtain an absolute 2.63% over the best baseline “SGNN+Int+Senti”
Summary
A script is a sequence of stereotypical events related to a protagonist. A restaurant script about “Jack” can consist of “Jack walked to a restaurant”, “Jack read the menu”, “Jack ordered food” and “Jack ate food”. Script learning aims to predict the subsequent event given the event chain. The first line models the event co-occurrence to infer the event. Event pair methods like PMI (Chambers and Jurafsky, 2008) and event bigram (Jans et al, 2012) aim to model the pair relation between predicted event and events within the chain. PairLSTM (Wang et al, 2017) and SAM-Net (Lv et al, 2019) both utilize event pair and event chain modeling to predict the subsequent event. The second line goes beyond the event-occurrence and utilizes discourse relations or external commonsense knowledge. (Lee and Goldwasser, 2019) utilizes Trans*(TransE,TransR,TransH) to learn the relation between events and (Ding et al, 2019) aims to integrate external commonsense knowledge like sentiment and intention into event representations. The above studies ignore the effect of external event knowledge, which can provide abundant temporal and causal knowledge for events
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