Event Representation Learning Enhanced with External Commonsense Knowledge

Event Representation Learning Enhanced with External Commonsense Knowledge Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, Junwen Duan Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, China {xding, kliao, tliu, zyli, jwduan }@ir.hit.edu.cn Abstract Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market 1 . 1 Introduction Events are a kind of important objective information of the world. Structuralizing and representing such information as machine-readable knowledge are crucial to artificial intelligence (Li et al., 2018b, 2019). The main idea is to learn distributed representations for structured events (i.e. Figure 1: Intent and sentiment enhanced event embed-dings can distinguish distinct events even with high lexical overlap, and find similar events even with low lexical overlap. The function maps the summed vectors into an event embedding space.