# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 1 Introduction
### Challenges in Arabic Event Coreference
When transferring NLP models from English to Arabic or any other language, it is imperative to understand the linguistic and domain-specific characteristics of both languages and datasets. The key to achieving good performance in cross-lingual NLP tasks is careful analysis, adaptation, and evaluation. In addition, Arabic has several distinctive features, including its root-based morphology, its right-to-left script, and its complex verb conjugation. These linguistic differences could challenge models that are trained on Indo-European languages such as English. A key difference between within-document event coreference resolution and cross-document event coreference resolution lies in the scope and objective of each task. As opposed to within-document resolution, which emphasizes coherence within a single text by connecting mentions of events, cross-document resolution focuses on connecting and consolidating information about events across multiple texts to improve understanding of events that occur in various contexts. Among the NLP tasks, both event-centered analysis and information retrieval have their own challenges and applications. It is more challenging to resolve cross-document event coreferences than to resolve within-document events because it often involves reasoning about the same event in various contexts, dealing with  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Lemma. ** & **English Inflectional Forms** & **Arabic Inflectional Forms** \\ \hline attack— (hujm) & attack, attacks, attacking & _\"A_\" ([PERSON] visited the library) and \"A_\",\"S_\" (I visited the library). In the second sentence, the verb form \"A_\",\" is different due to the speaker being first-person singular, making it challenging to link this event to the first sentence. **Dropped Pronouns**: Arabic frequently drops subject pronouns due to its tendency to drop them, making it more difficult to determine the agents of events, which are crucial for resolving coreferences [4]. For instance, in this sentence \"A_\",\"S_\" (Went to school)\" the subject pronoun \"U_\" (I) is dropped, which makes it more difficult to determine the agent of the event. **Pronouns Ambiguity**: Due to the extensive use of pronouns with multiple forms and genders, Arabic pronouns are highly ambiguous, as are other Arabic words. For example, in this sentence: \"A_\",\"S_\",\"S_\" (She told her that she loves him)\" both pronouns “A_\" and \"A_\" refer to different entities. Based on context, it is necessary to disambiguate pronoun referents in order to resolve coreferring events accurately. **Events Ellipses**: These refer to situations where an event is referred to or implied without being explicitly mentioned. Arabic uses events ellipses extensively. In this sentence, \"A_\",\"S_\",\"S_\",\"S_\",\"S_\" (The boy ate an apple and the girl ate an orange)\", the event of eating is referred to twice, but the second time it is implied without being explicitly mentioned in a sentence. By recognizing event ellipsis, the coherence of discourse is enhanced as it aids in resolving coreferring events accurately. **Cultural and Contextual Sensitivity**: The interpretation and resolution of Arabic coreferences may involve a consideration of cultural and contextual factors specific to the Arabic language and its diverse dialects. In order to address these challenges, a deep understanding of Arabic linguistics, advanced natural language processing techniques, and the availability of annotated resources specific to Arabic event coreference resolution are required. When transferring NLP models from English to Arabic or any other language, it is imperative to understand the linguistic and domain-specific characteristics of both languages and datasets. The key to achieving good performance in cross-lingual NLP tasks is careful analysis, adaptation, and evaluation. In addition, Arabic has several distinctive features, including its root-based morphology, its right-to-left script, and its complex verb conjugation.