Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph

Abstract

Bin packing is a classic optimization problem with a wide range of applications from load balancing to supply chain management. In this work, we study the online variant of the problem, in which a sequence of items of various sizes must be placed into a minimum number of bins of uniform capacity. The online algorithm is enhanced with a (potentially erroneous) prediction concerning the frequency of item sizes in the sequence. We design and analyze online algorithms with efficient tradeoffs between the consistency (i.e., the competitive ratio assuming no prediction error) and the robustness (i.e., the competitive ratio under adversarial error), and whose performance degrades near-optimally as a function of the prediction error. This is the first theoretical and experimental study of online bin packing in the realistic setting of learnable predictions. Previous work addressed only extreme cases with respect to the prediction error, and relied on overly powerful and error-free oracles.

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

“…Recently, document-level event extraction (DEE) attracts great attention from both academic and industrial communities, and is regarded as a promising direction to tackle the above issues Zheng et al, 2019;Xu et al, 2021b;Yang et al, 2021;Zhu et al, 2021). However, by our observation, we discover that the relations between event arguments have patterns which are an important indicator to guide the event extraction.…”

“…At first, the event is identified from a central sentence and other arguments are extracted from neighboring sentences separately . Later, an innovative end-to-end model Doc2EDAG, is proposed (Zheng et al, 2019) (Zhu et al, 2021). In summary, although those existing works target for solving across-sentence and multi-event issues of the DEE task from various perspectives, to our best knowledge, we conduct a pioneer investigation on relation modeling towards this research field in this paper.…”

“…4) GIT (Xu et al, 2021b), a model using heterogeneous graph interaction network as encoder and maintaining a global tracker during the decoding process. 5) PTPCG (Zhu et al, 2021), a light-weighted and latest DEE model.…”

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

“…The missing parts are caused by the inaccessibility of baseline codes. †: results from (Xu et al, 2021b); ♠: results from (Yang et al, 2021); ♣: results from (Zhu et al, 2021). 6 shows the comparison results of all baselines and ReDEE.…”

“…The results in Table 6: Comparison of event extraction between singular (S.) and multiple (M.) event documents on the ChiFi-nAnn. †: results from (Xu et al, 2021b); ♠: results from (Yang et al, 2021); ♣: results from (Zhu et al, 2021). tribution than RAAT-1, with a decrease of 0.7% versus 0.4% in F1 scores once been substituted.…”