# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 2 Various Approaches and Data
### Approaches
The CoNLL score is the average of the first three metrics [15]. resolution have encountered competition from transformer-based approaches. These newer techniques employ large language models to produce powerful contextual representations of mentions, forming the foundation for their classification algorithms [22]. Transformer-based approaches that are span-based [23] have demonstrated state-of-the-art performance in the English language. It is important to note that these pre-trained language models are optimized to encode longer word sequences, which results in more robust contextual representations of events (including multi-word events). Even though mention-pair models generally perform better in coreference resolution tasks, one of their main limitations is their inability to account for event coreference chains involving more than two events. Rather than considering the entire discourse, the algorithm reduces to making pairwise decisions. The second paradigm, mention-ranking, addresses some of the limitations of the mention-pair approach. Based on the feature representation of the mention and its antecedents, the possible antecedents of a mention are ranked. The algorithm calculates the probability of all co-referential relationships [24] based on a partitioning of co-referential chains. A third method of resolving event correlations is known as the easy-first modeling approach. An event coreference approach is applied using rule-based multi-pass sieve algorithms that have been found to be successful in entity coreference research [25]. A system in which mentions which are relatively \"easier\" are resolved first is determined by a combination of a series of classification rules or sieves arranged in decreasing order of precision. However, it is possible to include global coreference cluster information even though rule systems are primarily based on pairwise comparisons. As a result, mention-pair approaches are addressed, albeit to a minor extent. In addition, within-chain event argument propagation [26] and agglomerative clustering [27] can further improve the performance of simple first methods. Event coreference are resolved using gold-standard event mentions in the methods and algorithms discussed thus far. End-to-end systems must, however, first extract mentions from raw text. To resolve coreferences end-to-end, a pipeline or a joint approach can be used. It is possible to detect and resolve event mentions in a pipeline configuration using several different methods, and any self-contained detection method may be coupled with any of the above methods for resolving events. In spite of the fact that such systems can be relatively easy to implement and highly customizable, they are prone to error propagation, since errors in one component can pass without being corrected to the next. Alternatively, joint event coreference resolution aims to model both event detection and coreference resolution simultaneously. Integer linear programming and Markov logic networks can be used to perform joint inference [28; 29]. Each component can be enhanced by incorporating background knowledge. By utilizing segment-based decoding, a joint coreference resolution algorithm can be generated as part of a full-blown joint-learning approach, which combines the two tasks into one structured prediction task. It has been demonstrated that joint methods, particularly joint inference methods, perform best in this field [30], particularly when combined with high-performance entity coreference resolution systems [15] and transformer-based architectures [23].