# Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction
## 1 Introduction  
As [PERSON] said, the past does not repeat itself, but it rhymes. We may be able to learn the evolution of events from past events. Taking the news reports of _\"2007 Peru earthquake\"_ and _\"2010 Chile earthquake\"_ as an example (see Figure 1), the same event background and evolution path exist in news reports from different periods (events are all evolved along with the background of _\"earthquake\"_, _\"tsunami warning\"_, _\"personal casualty\"_, and _\"disaster assistance\"_). This demonstrates that similar events have similar backgrounds, and historical events may provide a wealth of prior knowledge for event prediction. Event prediction is very important for government decisions [1; 2; 3] and financial investment [4; 5]. Governments can avoid casualties and property damage based on disaster warnings. At the same time, they also can predict social trends and understand public opinion. Financial institutions can estimate investment risks to avoid potential economic losses. Because of the high application value of event prediction, research interest in this field is increasing. Some research uses traditional machine learning methods for event prediction. These methods apply textual characteristics of events, statistical language models, etc. for event prediction. For example, [PERSON] et al. [6] used the timestamp, text similarity, and temporal proximity to infer the event evolutional relationship. [PERSON] et al. [7] proposed a method for event prediction based on an n-gram language model. In recent years, many research attempts to use neural network in event prediction tasks. Most methods apply Recurrent Neural Network (RNN), a neural network that processes sequence data, for event prediction. [PERSON] et al. [8] described a Long Short-Term Memory (LSTM) model for statistical script learning. [PERSON] et al. [9] used LSTM hidden states as features for event pair modeling and utilized a dynamic memory network to infer subsequent events. Although the above methods have achieved good success, there are still some shortcomings of prediction. Firstly, current methods based on event pairs can only judge the evolution relationship between events that already exist in the training data, while it is difficult to represent the evolution direction of events. Secondly, time series-based event prediction may ignore the influence of earlier events, which will cause some bias in the direction of event evolution. Finally, the event description has few words, which cannot contain the rich semantics of events. How can we supplement the event description? To overcome these problems, we propose a novel model, called Hierarchical Semantic Gated Recurrent Unit (HS-GRU), for event prediction. HS-GRU extracts time-based event information and prior knowledge of current events separately, and fuses them by the semantic selective attention mechanism to predict the next possible event. The input of HS-GRU is an event link which is the sequence of related events in a short period. Of these, the current event link for prediction provides time-based event information, and historical event links provide prior knowledge for the current event prediction. The output of model is a textual description of the next event. HS-GRU includes three layers. The first layer encodes time-based event information and prior knowledge of the current event link. Among them, the model adopts Gated Recurrent Unit (GRU) [10] to collect time-based event information in the current event link. We use Associated Link Network (ALN) [11] to capture prior knowledge of the current event link. ALN extracts the semantic background related to the current event link from historical event links as the prior knowledge of event prediction. The second layer fuses time-based event information and prior knowledge of the current event link. The model applies the semantic selective attention mechanism to calculate the impact score of events in the current event link under the prior knowledge.