Boundaries and edges rethinking: An end-to-end neural model for overlapping entity relation extraction

DWIE: An entity-centric dataset for multi-task document-level information extraction

“…Initially, this approach has been applied to improve the performance of the single coreference resolution task by transferring document-level contextual information between coreferenced entity mention spans (Kantor & Globerson, 2019;Lee et al, 2018). Most recently, these graph propagation techniques have been successfully used in a joint setting (Fei et al, 2020;Fu et al, 2019;Luan et al, 2019;Wadden et al, 2019) by performing graph message passing updates between the shared spans across different tasks. However, while successful on mention-driven datasets such as ACE 2005 (Walker et al, 2006) and NYT (Riedel et al, 2010), as far as we are aware, the advantages of these techniques have not yet been investigated in an entity-centric documentlevel setting.…”

“…Current dominant IE systems consider mention-level scoring of NER as well as RE components when reporting on datasets such as CoNLL-2003(Akbik et al, 2019Baevski et al, 2019;Chiu & Nichols, 2016;Lample et al, 2016), OntoNotes (Chiu & Nichols, 2016;Clark et al, 2018;Strubell et al, 2017), ACE 2004 (Bekoulis et al, 2018a;Li & Ji, 2014;Zhang et al, 2017a), ACE 2005(Fei et al, 2020Luan et al, 2019;Zhang et al, 2017a), TACRED (Soares et al, 2019;Zhang et al, 2018Zhang et al, , 2017b, and SelEval 2010-Task 8 (Guo et al, 2019;Hu et al, 2020;Peters et al, 2019) among others. In contrast, the DWIE dataset is entity-centric where all the annotations are done on the entity cluster level.…”

“…It is based on the span-based architecture introduced in Lee et al (2017), which supports training on the space of all entity spans simultaneously, dynamically updating span representations by using the graph propagation approach (further detailed in Section 4.4). Recent works have shown that this idea has the potential for improved effectiveness (albeit at a higher computational cost) (Dixit & Al-Onaizan, 2019;Fei et al, 2020;Lee et al, 2018;Luan et al, 2019), compared to more traditional sequence-labeling approaches (Katiyar & Cardie, 2018;Lample et al, 2016;Luan et al, 2017;Ma & Hovy, 2016). More concretely, the use of a span-based approach where all the spans are shared between the individual task modules avoids the cascading of errors from the entity mention identification module (entity scorer in Fig.…”

“…These models allow message passing between local contextual encodings, making it possible to measure the impact of local contextual information sharing both on a more general document level and across the tasks. Furthermore, previous work already has shown the positive effect of using graph-based information passing techniques on single tasks (Kantor & Globerson, 2019;Lee et al, 2018), and between tasks (Fei et al, 2020;Fu et al, 2019;Luan et al, 2019;Wadden et al, 2019) on mention-driven datasets. We expand this work even further by extending these models to be used on the entity-centric, document-level DWIE dataset.…”

“…The last decade has shown a growing interest in IE datasets suitably annotated for developing multi-task models where each of the tasks (e.g., NER, RE, etc.) would benefit from the interaction with (an)other task(s) (Bekoulis et al, 2018b;Fei et al, 2020;Lee et al, 2017Lee et al, , 2018Luan et al, 2019), to boost their performance. However, the currently widely used IE datasets to build such multi-task models exhibit three major limitations.…”