# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 2 Various Approaches and Data
### Approaches  
According to Table 5, in their model [16], multiple representations are learned and integrated from both event alone and event pair data. In order to create more discriminatory representations of events, they introduced multiple linguistically motivated event alone features. To capture the distinctions between event pairs, they considered multiple similarity measures. They demonstrate the effectiveness of their proposed model by achieving a state-of-the-art on the ACE2005 benchmark. The model is also compared with ground truth triggers and predicted event-alone features in order to ensure a thorough comparison with the CDGM model. A new model for event coreference resolution, called EPASE, was proposed in [17]. It can cover event paraphrases in a broader range of situations, improving generalization, by identifying deep paraphrase relationships within an event-specific context of sentences. In addition, argument roles are embedded in event embedding without relying on a fixed number or type of arguments, leading to greater EPASE scalability. There is consistent and significant superiority of this method over existing methods, both within- and cross-document correlations. The resolution of event coreferences is an important research problem with many applications. Although pre-trained language models have achieved remarkable success in recent times, we argue that using symbolic features is still highly beneficial. The automatic extraction of symbolic features is subject to noise and errors, since reference resolution typically comes from upstream components in the information extraction pipeline. Furthermore, certain features may be more informative than others depending on the context. In response to these observations, the authors in [18] proposed a novel context-dependent gated module for adaptively controlling the information flow from the input symbolic features. With the help of a simple noisy training method, their proposed models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016. According to reference [15], event coreference resolution typically follows the same paradigms as entity coreference resolution. It should be noted, however, that the methods described in the following discussion are limited to the English and Chinese languages. Coreference resolution is currently characterized by three predominant paradigms. Typically, the mention-pair approach is used, which involves transforming the process of forming clusters of co-referential mentions into a binary classification process. Based on this approach, pairs of event mentions are generated and classified using a binary classification algorithm. In order to reconstruct the event coreference chain from the binary output, a clustering algorithm is used. Recently, mention-pair systems have evolved in tandem with developments in machine learning methods frequently used in natural language processing. Prior to deep neural networks [19], support vector machines [20] and decision trees [4] were used to analyze feature-based data. In studies, outward lexical similarity has been demonstrated to be the most powerful indicator of coreference among these approaches, and features based on string comparisons have also been demonstrated to be the most powerful. A further feature that models the document's structure was also successful in resolving coreference in its context [15; 21]. However, feature-based methods for coreference \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dataset** & **CoNLL** & **AVG** \\ \hline End-to-End within-document [16] & ACE2005 & 64.56 & 62.11 \\ \hline Gold triggers within-document [16] & ACE2005 & 86.78 & 86.63 \\ \hline EPASE-within-document [17] & ECB+ & 88.3 & - \\ \hline EPASE-cross-document [17] & ECB+ & 84.3 & - \\ \hline End-to-End within-document [18] & KBP 2016 & 43.55 & 40.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: English state-of-the-art systems in event coreference. The AVG is the average F-score of four metrics: MUC, B3, CEAFe and BLANC.