Improving Event Detection via Open-domain Trigger Knowledge (2020)

Abstract: Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git .

## introduction event detection (ed) aims at detecting trigger words in sentences and classifying them into predefined event types, which shall benefit numerous applications, such as summarization @xcite and reading comprehension @xcite . for instance, in s1 of figure 1 , ed aims to identify the word fire as the event trigger and classify its event type as attack. mainstream researches @xcite @xcite @xcite focus on the second step event type disambiguation via lexical and contextual features. however, it is also crucial to identify trigger words correctly as the preliminary step. trigger word identification is a non-trivial task, which suffers from the long tail issue. take the benchmark ace2005 as an example: trigger words with frequency less than 5 account for 78.2% of the s1: now we 're hearing the boom of iraqi guns as they fireattack towards our positions . total. the long tail issue makes supervised methods @xcite prone to overfitting and perform poorly on unseen/sparsely labeled triggers @xcite . automatically generating more training instances seems to be a solution: expanding more instances by bootstrapping @xcite @xcite and expending more data from distantly supervised methods @xcite . however, the performance of these methods on unseen/sparsely labeled trigger words is still unsatisfied, as shown in in the paper, we empower the model with external knowledge called open-domain trigger knowledge to provides extra semantic support on unseen/sparsely labeled trigger words and improve trigger identification. open-domain trig- method unseen sparse dense dmbert sup-only 54.4 72.5 84.1 bootstrapsemi-sup 56.6 73.6 86.9 dgbert distant-sup 54.7 72.8 84.3 ger knowledge is defined as a prior that specifies which words can trigger events without subject to pre-defined event types and the domain of texts. as shown in s1 of figure 1, open-domain trigger knowledge can identify that hearing and fire as event triggers, even if hearing does not fit into any pre-defined event types in ace2005. with opendomain trigger knowledge, we are able to discover unseen/sparsely triggers from the large-scale unlabeled corpus, which will improve the recall in trigger words identification. however, it is challenging to incorporate open-domain trigger knowledge into ed: triggers identified by open-domain trigger knowledge do not always fit well with indomain labels, and thus can not be directly adopted as the trigger identification result. for example in s4 of figure 1 , open-domain trigger knowledge argues that exploded is the trigger word, while under the labeling rules of ace2005, intifada is the trigger word. specifically, we propose an enrichment knowledge distillation (ekd) model to efficiently distill open-domain trigger knowledge from both labeled and abundant unlabeled corpora. we first apply a light-weight pipeline to equipment unlabeled sentences with trigger knowledge from wordnet. the method is not limited to specific domains, and thus can guarantee the coverage of trigger words. then, given the knowledge enhanced data as well as ed annotations, we train a teacher model for better performance; meanwhile, a student model is trained to mimic teacher's outputs using data without knowledge enhancement, which conforms to the distribution during inference. we further promote the generalization of the model by adding noise to the inputs of the student model. we evaluate our model on the ace2005 ed benchmark. our method surpasses nine strong baselines, and is especially effective for unseen/sparsely labeled triggers word. experiments also show that the proposed ekd architecture is very flexible, and can be conveniently adapted to distill other knowledge, such as entity, syntactic and argument. our contributions can be summarized as: detailed studies show that our method can be conveniently adapted to distill other knowledge, such as entities. ## methodology in the section, we introduce the proposed enrichment knowledge distillation (ekd) model, which leverages open-domain trigger knowledge to improve ed. in general, we have a teacher model and a student model. the teacher is fully aware of open-domain trigger knowledge, while the student is not equipped with open-domain trigger knowledge. we make the student model to imitate the teacher's prediction to distill the open-domain trigger knowledge to our model. figure 2 illustrates the architecture of the proposed ekd model. during training, we first pre-train the teacher model on labeled data, and then force the student model, under the knowledge-absent situation, to generate pseudo labels as good as the teacher model on both labeled and unlabeled data. by increasing the cognitive gap between teacher and student model, the student model has to learn harder. we first introduce how to collect the opendomain trigger knowledge in knowledge collection. we then illustrate how to exploit the labeled data to pre-train the teacher model in feature extraction and event prediction. finally, we elaborate on how to force the student model to learn from the teacher model in knowledge distillation. ## conclusion we leverage the wealth of the open-domain trigger knowledge to address the long-tail issue in ace2005. specifically, we adopt a wordnet-based pipeline for efficient knowledge collection, and then we propose a teacher-student model, ekd, to distill open-domain trigger knowledge from both labeled and abundant unlabeled data. ekd forces the student model to learn open-domain trigger knowledge from teacher model by mimicking the predicted results of the teacher model. experiments show that our method surpasses seven strong knowledge-enhanced baselines, and is especially efficient for unseen/sparsely triggers identification.