L earn DA : Learnable Knowledge-Guided Data Augmentation for Event Causality Identification (2021)

Abstract: Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).

## introduction event causality identification (eci) aims to identify causal relations between events in texts, which can provide crucial clues for nlp tasks, such as logical reasoning and question answering @xcite @xcite . this task is usually modeled as a classification problem, i.e. determining whether there is a causal relation between two events in a sentence. for example in figure 1 , an eci system should identify two causal relations in two sentences: (1) attack cause -→ killed in s1; (2) statement cause -→ protests in s2. most existing methods for eci heavily rely on annotated training data @xcite kimani gray, a young man who likes football, was killed in a police attack shortly after a tight match. in the week following the fatal violence, several protests have erupted because of the official statement. ## related work to date, many researches attempt to identify the causality with linguistic patterns or statistical features. for example, some methods rely on syntactic and lexical features @xcite . some focus on explicit causal textual patterns @xcite @xcite @xcite . and some others pay attention on statistical causal association and cues @xcite @xcite . recently, more attention is paid to the causality between events. @xcite annotated causal-timebank of event-causal relations based on the tempeval-3 corpus. @xcite , @xcite extracted eventcausal relation with a rule-based multi-sieve approach and improved the performance incorporating with event temporal relation. @xcite annotated both temporal and causal relations in 320 short stories. @xcite annotated the eventstoryline corpus for dual cycle primal cycle causal/ non-causal relaiton causal/ non-causal sentence event pair annotated data pre-trained identifier pre-trained generator learnable dual augmentation architecture dual-trained identifier dual augmented data full-trained identifier pre-training further training knowledge relation->sentence->relation sentence->relation->sentence figure 2: overview of the learnable knowledge-guided dual data augmentation for eci. event causality identification. dunietz et al. (2017) presented because 2.0, a new version of the be-cause corpus (dunietz et al., 2015) of causal relation and other seven relations. gao et al. (2019) modeled document-level structures to identify causality. liu et al. (2020) identified event causality with the mention masking generalization. unlike computer vision, the augmentation of text data in nlp is pretty rare @xcite . @xcite solved the data lacking problem of eci with the distantly supervised labeled training data. however, including the distant supervision, most of the existing data augmentation methods for nlp tasks are task-independent frameworks (related work of data augmentation and dual learning are detailed in appendix b). inspired by some generative methods which try to generate additional training data while preserving the class label @xcite @xcite , we introduce a new learnable framework for augmenting task-related training data for eci via dual learning enhanced with external knowledge. ## methodology as shown in figure 2 , learnda jointly models a knowledge guided sentence generator (input: event pair and its causal/non-causal relation, output: causal/non-causal sentence) and an event causality identifier (input: event pair and its sentence, output: causal/non-causal relation) with dual learning. learnda iteratively optimizes identifier and generator to generate task-related training data, and then utilize new data to further train the identifier. therefore, we first present the main idea of dual learning, which is the architecture of learnable dual augmentation, including the states, actions, policies, and identifier @xmath0, s' event pair (ep) sentence (s) ep, c' r s r c r c r s primal cycle dual cycle r r i g figure 3: the architecture of learnable dual augmentation. causal and ncausal represent the causal and non-causal sentence generator respectively. red parts are the process @xmath1, @xmath2, while blue parts are the process @xmath3, @xmath4. solid and dashed lines denote the main process and reward feedback direction respectively. ## conclusion this paper proposes a new learnable knowledgeguided data augmentation framework (learnda) to solve the data lacking problem on eci. our framework can leverage the duality between generation and identification via dual learning to gener-ate task-related sentences for eci. moreover, our framework is knowledge guided and learnable. our method achieves state-of-the-art performance on eventstoryline and causal-timebank datasets. as shown in