K now D is: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision (2020)

Abstract: Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions, and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.

## introduction event causality detection (ecd) aims to identify causal relations between events from texts, which may provide crucial clues for many nlp tasks, such as information extraction, logical reasoning, question answering, and others @xcite @xcite . for example, the causal relation that kimani gray was killed because of a police attack is needed to be detected in the following sentence: "kimani gray, a young man who likes football, was killed in a police attack shortly after a tight match." this task is usually modeled as a classification problem, i.e. determining whether there is a causal relation between two events in a sentence. to this end, most existing methods adopt a supervised learning paradigm @xcite @xcite @xcite . although these methods have achieved good performance, they usually need large-scale annotated training data. however, existing event causality detection datasets are relatively small. for example, the eventstoryline corpus @xcite only contains 258 documents, 4316 sentences, and 1770 causal event pairs. these small datasets are in low coverage of causal expressions and obstacle nlp applications deployed on large-scale data. recent improvements of distant supervision have been proven to be effective to label training data for some tasks, such as relation extraction @xcite , event detection @xcite , and so on. therefore, we investigate a distant data augmentation framework for solving the data lacking problem on the ecd task, dubbed as knowledge enhanced distant data augmentation (knowdis), to automatically label available data. we argue that a sentence contains an event pair with a high probability of causality and expresses its causal semantic can be labeled as training data for the ecd task. to automatically label a large number of training data, we need to solve the following three challenges. (1) how to collect a large number of event pairs with a high probability of causality and employ them to label training data. (2) how to handle noisy distantly labeled sentences that do not have well-formed textual expressions to express causal semantics. (3) how to make better use of distantly labeled sentences for training. to this end, we firstly design a lexicon enhanced annotator (lexianno) to extract a large number of event pairs with a high probability of causality based on lexical knowledge and employ them to automatically label sentences via distant supervision. secondly, we propose a commonsense filter (commonfilter) to refine distantly labeled sentences assisted with causal commonsense knowledge which makes them more well-formed to express the causal semantics. thirdly, we employ relabeling and annealing strategies to make better use of distantly labeled sentences for training. finally, we evaluate knowdis on two datasets and achieve the best performance training with distantly labeled sentences on ecd. the following sections describe the architecture (section 2) of knowdis and the experimental results (section 3) on the ecd task. ## knowdis as shown in figure 1 , we illustrate the three main components of our proposed knowdis in this section. ## conclusion in this paper, we try to employ distant supervision to alleviate the data lacking problem on causal-related task. we propose a knowledge enhanced distant data augmentation framework (knowdis) for event causality detection. our method achieves the sota performance on eventstoryline corpus and causal-timebank dataset assisted with knowledge enhanced distantly labeled training data. in the future, we will introduce more causal-related resources and apply knowdis for other relational tasks.