Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022)

Abstract: Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is event sequence prediction , which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events. However, this statistical based approach is complex and predicts a limited variety of events. At the same time this approach ignores the rich knowledge of external events that is important for predicting event sequences. In this paper, in order to predict more diverse events, we first reformulate the event sequence prediction problem as a sequence generation problem. Then to leverage external event knowledge, we propose a three-stage model including augmentation, retrieval and generation. Experimental results on the event sequence prediction dataset show that our model outperforms existing methods, demonstrating the effectiveness of the proposed model.

## introduction inferring events related to a specific target is one of the capabilities pursued by natural language understanding, and it is also very helpful for other natural language processing (nlp) tasks, such as event extraction @xcite ), text summarization @xcite and discourse understanding @xcite . a challenging task in this direction is event sequence prediction. specifically, given a goal, the task aims to predict a sequence of events that fits the goal. figure 1 shows an example of the event sequence prediction task. given the goal buy a mobile phone, it is expected to predict an event sequence, including four events determine the brand, determine the price range, select the brand series and pay the bill. ## our framework given a goal g, the event sequence prediction task needs to predict an event sequence (e 1 , e 2 , ..., e i , ..., e n ) that fits the goal, where e i is the predicted i-th event. here both the goal g and the event e i are mainly composed of a verb and an object. figure 2 shows the framework of our model which consists of three modules, namely augmentation, retrieval and generation. below we introduce these modules separately. ## conclusion in this paper, we first reformulate the event sequence prediction task as an event sequence generation task, which can generate a wider variety of events. we then propose a three-stage model including augmentation, retrieval, and generation in order to leverage an external event knowledge base to generate event sequences. finally, exper-imental results show that our model outperforms existing methods, demonstrating the effectiveness of the proposed method.