Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection (2024)

Abstract: Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. The task typically relies on trigger detection (TD) – identifying token spans in the text that evoke specific events. While the notion of triggers should ideally be universal across domains, domain transfer for TD from high- to low-resource domains results in significant performance drops. We address the problem of negative transfer in TD by coupling triggers between domains using subject-object relations obtained from a rule-based open information extraction (OIE) system. We demonstrate that OIE relations injected through multi-task training can act as mediators between triggers in different domains, enhancing zero- and few-shot TD domain transfer and reducing performance drops, in particular when transferring from a high-resource source domain (Wikipedia) to a low(er)-resource target domain (news). Additionally, we combine this improved transfer with masked language modeling on the target domain, observing further TD transfer gains. Finally, we demonstrate that the gains are robust to the choice of the OIE system.

## introduction event detection is an important part of the information extraction pipeline in natural language processing (nlp). event detection systems are typically bound to domain-specific schemes and fill predefined event-specific slots evoked by an event trigger -a span of words that evokes a particular type of event. a typical domain-specific event detection workflow consists of trigger detection (td), which locates the trigger span in the text, and trigger classification @xcite , which assigns one of the predefined event types to the trigger. with triggers identified, the next step is typically ## oie for event trigger detection following prior work @xcite , we frame td as a sequence labeling task where each token is classified as either part of some event trigger span or outside of it. this task formulation is intuitive, given that event triggers are consecutive token sequences, and multiple triggers may appear in the same input sentence. we use the widely adopted iob2 (inside, outside, begin) tagging scheme @xcite . analogously, we model relation extraction (re) -for which we use oie relation extractions as ground-truth labels -also as a sequence labeling task with its own set of iob2 tags. we tackle domain transfer for td with two different model architectures (based on a plm) that couple oie relations with td annotations, which we refer to as (i) implicit and (ii) explicit oie-td multi-task models. we next describe both variants in detail. implicit multi-task. in the implicit model, we train and use embeddings for token labels of oie relations: one randomly initialized vector for each of the three iob2 tags. the model concatenates the embedding x @xmath0, where d is the dimension of the trainable oie relation label embeddings (hyperparameter of the model), and h is the plm's hidden size. the final token representation, @xmath1; we hypothesize that the implicit model is incentivized to establish -within the oie label embeddings trained via event td -contextualized associations between the two tasks. intuitively, this should improve the recall of td in the target domain as long as the oie -which is rule-based and thus more domain agnostic -is resilient to distribution shifts between domains. similar event detection approaches based on training label embeddings exist @xcite @xcite . however, they typically concatenate the label and token embeddings at the encoder's input and rely on encoders shallower than common transformer-based plms. ## experimental setup our experiments investigate the transfer from a high-resource source domain to a low-resource target domain, which is the common transfer direction. for facilitating few-shot domain transfer of td, we employ joint and sequential transfer training regimes in combination with multi-task models. ## results and discussion ## conclusion we showed that oie relations can be utilized to improve the domain transfer of trigger detection (td) in zero-and few-shot setups. the best improvements were achieved with implicit multi-task model and sequential transfer training regime. we also demonstrated that more substantial gains can be reached when combining oie relations with mlm as an auxiliary task. this is especially evident for the models pre-trained with td task on the source domain and with mlm training objective on the target domain in the implicit multi-task model. replacing minie with stanford oie revealed that gains on the target domain for the td task persist when using the other oie extractor. future work may further explore the potential of oie for improving domain transfer of td on diverse datasets and domains, such as the cybersecurity @xcite , literature @xcite , and biomedical @xcite domains. applying the coupling concept to other nlp tasks, such as event argument detection or named entity recognition, where oie extractions might enhance the in-and out-of-domain performance, is another exciting future work direction.