Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction (2022)

Abstract: We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets evaluated in an entity-centric way. Especially, we address the two-way interaction between COREF and RE that has not been the focus by previous work, and propose to introduce explicit interaction namely Graph Compatibility (GC) that is specifically designed to leverage task characteristics, bridging decisions of two tasks for direct task interference. Our experiments are conducted on DocRED and DWIE; in addition to GC, we implement and compare different multi-task settings commonly adopted in previous work, including pipeline, shared encoders, graph propagation, to examine the effectiveness of different interactions. The result shows that GC achieves the best performance by up to 2.3/5.1 F1 improvement over the baseline.

## introduction there has been a growing interest in documentlevel relation extraction recently since the introduction of several large-scale datasets such as docred @xcite , which requires inter-sentence reasoning over the global entities and classifies relation instances on the entity-level, with each entity being a cluster of coreferent mentions across a document. in this line of entity-centric research, recent work has made great advancement on the global reasoning while regarding the entities as given @xcite @xcite . nevertheless, the more practical end-to-end setting that extracts global entities and relations jointly has not drawn much attention, which poses extra burden to the model that needs to resolve mentions, coreference and relations at once. in this work, we specifically address this end-to-end setting such that given a document, the model targets to extract all gold triples (e h , e t , r), where an instance is evaluated as correct only if the head/tail entity clusters (e h /e t ) as well as the relation r are all correct. to leverage the potentials that different tasks could benefit from each other, two popular methods have been taken by recent span-extraction-based models. one is to simply share the encoder (hence sharing mention representation) in the multi-task learning while decoding separately in a pipeline manner @xcite . the other is to add graph propagation that enriches mention representation with task-specific decisions, e.g. dygie @xcite . however, the task interactions above only happen on the representation level, and still employ the pipeline-like decoding, thus no explicit interactions have been made that directly interfere the decisions of different tasks. meanwhile, the improvement from graph propagation has been diminished under strong encoders like bert @xcite that are able to model long-range dependency, as shown by recent work @xcite @xcite . therefore, aiming to further improve performance, we focus on the task interactions in this work and propose to introduce explicit interactions that utilize unique task characteristics, mitigating negative effects such as error propagation from the pipeline decoding. specifically, in addition to the regular scoring on mention pairs for coreference resolution which is itself independent from relation classification, we add a second source of coreference scores from relation scores, exploiting the clue that for a pair of mentions (m x , m y ) that refer to the same entity, their relation scores s r should be similar when paired with any other mentions m k , as s r (m x , m k ) ≈ s r (m y , m k ); conversely, for a noncoreferent pair, their relation scores towards other mentions tend to be divergent. we then formulate the relation scores s r for each mention as a local graph, and learn a distance metric as the secondary coreference score that checks the compatibility of local graphs of a mention pair. the added term acts as a bridge between coreference and relations, thereby providing explicit task interactions that circumvents independent decoding of each task. to have a systematic evaluation of our approach, we implement and conduct our experiments in five multi-task settings ( §2), ranging from the pipeline approach to three different interaction methods that compare the impact of task interactions for document-level ie. empirical results on two entitycentric datasets, docred and dwie, show that simple representation sharing can indeed consistently bring marginal improvement over the naive pipeline approach, while both our adapted graph propagation method (as an implicit interaction) and our proposed explicit interaction method are able to further boost the performance by up to 2.3/5.1 f1 on two datasets. results suggest that explicit interactions serve as inter-task regularization that outperforms graph propagation, highlighting the importance of designing task-specific interactions in joint ie tasks. ## experiments above five settings are evaluated on two datasets: docred @xcite that consists of wikipedia documents, and dwie @xcite that consists of news articles. for docred, we follow the provided split and obtain the re scores on the test set by submitting predictions to its official codalab competition. dwie does not come with a pre-defined dev set; we randomly holdout 10% training set for model tuning, while using the entire training set in the final evaluation to be consistent with previous work. details and statistics of the two datasets are provided in a.3. implementation our baseline implementation is adapted from the pytorch coref model by @xcite and the atlop re model by @xcite . the proposed joint-m, +gp, +gc models are further coded in pytorch. for all experiments, we use spanbert-base @xcite as the encoder which we found performs slightly better than bert. more implementation details and hyperparameters are provided in a.4. ## conclusion we address the task interactions in the end-to-end document-level relation extraction, and compare five model settings featuring different interactions, including both implicit and our proposed explicit interaction that bridges between coref and re. experiments show that all interactions can boost performance, while the explicit interaction is shown more effective comparing with others, obtaining the best performance on docred and dwie.