A Trigger-Aware Multi-Task Learning for C hinese Event Entity Recognition (2021)

Abstract: This paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific event type. Currently there is no specific model to directly deal with the EER task. Previous namedentity recognition methods that combine both relation extraction and argument role classification(named NER+TD+ARC) can be adapted for the task by utilizing the relation extraction component for event trigger detection (TD). However these technical alternatives heavily rely on the efficiency of the event trigger detection which have to require the tedious yet expensive human la-beling of the event triggers especially for languages where triggers contain multiple tokens andhave numerous synonymous expressions (such as Chinese). In this paper a novel trigger-awaremulti-task learning framework (TAM) which jointly performs both trigger detection and evententity recognition is proposed to tackle Chinese EER task. We conduct extensive experimentson a real-world Chinese EER dataset. Compared with the previous methods TAM outperformsthe existing technical alternatives in terms of F1 measure. Besides TAM can accurately identifythe synonymous expressions that are not included in the trigger dictionary. Morover TAM canobtain a robust performance when only a few labeled triggers are available.

## introduction in this paper, we introduce a variant of named entity recognition (ner) task, which is event entity recognition (eer). different from ner task, eer aims at identifying the named entities corresponding to a specific event type. figure 1 demonstrates some examples of eer. in example a, text contains both the event type '交易违规' (illegal trading) and '涉嫌传销' (illegal pyramid selling). obviously, the corresponding entity of event type '交易违规' (illegal trading) is the 'organization b'. hence, the 'organization b' is defined as the event entity of the query event '交易违规' (illegal trading). similarly, the event entity of query event '涉嫌传销' (illegal pyramid selling) is 'organization c'. clearly, eer can be seen as a selective ner task, which treats the 'text' and the 'query event' as the input, and identify the 'event entity' as output. eer is widely useful in many semantic applications such as public opinion analysis in financial domain, disease extraction in medical domain, etc. currently, however, there is no specific model to deal with eer task. to the best of our knowledge, mainly three types of techniques can be used to possibly achieve eer: vanilla ner models, questionanswer (qa) models and ner+td+arc models. for the vanilla ner models, solving eer task may be difficult due to the lack of event information. qa models can effectively incorporate the event information in the form of 'question' input. however, how to derive the appropriate question with respect to eer is still unknown. the ner methods that incorporate both the relation extraction and argument role classification are the most relevant solutions to tackle eer. specifically, we could adapt the relation extraction component for event trigger detection. these solutions extract all the named entities and the event triggers (i.e., trigger detection (td)) in a sentence, and then identify the relation between each named entity and the target event trigger respectively (i.e., argument role classification (arc)). here, an event trigger is a word indicating the event type mentioned in the text. there are various forms of ner+td+arc models. @xcite proposed a pipelined dmcnn model to perform td and ## the proposed algorithm the proposed tam mainly consists of two components: trigger detection network for event trigger detection and event-featured transformer-crf for event entity recognition. the architecture of the proposed tam is illustrated in figure 2 . at first, we briefly describe the process to build the event trigger dictionary. ## conclusion this paper proposed a trigger-aware multi-task model (tam) to deal with a novel event entity extraction (eer) task. tam is composed by two subtasks:a trigger detection network for event trigger identification and an event-featured transformer-crf for event entity recognition. the former effectively tackles the intractable chinese event trigger detection, where triggers usually contain multiple tokens and have numerous syn-onymous expressions. experiments show that the trigger detection network is effective for identifying the synonymous expressions of the labeled event triggers, which is useful to the downstream eer subtask, which is also robust against the availability of labeled triggers. the latter incorporates three kinds of event type relevant features and avoid the dependency modeling between the event trigger and arc existed in the relevant techniques. the experimental results demonstrate the superiority of the proposed tam against the existing sota technical alternatives. future work will be dedicated in further improving the performance of trigger detection.