Coreference resolution, critical for identifying textual entities referencing the same entity, faces challenges in pronoun resolution, particularly identifying pronoun antecedents. Existing methods often treat pronoun resolution as a separate task from mention detection, potentially missing valuable information. This study proposes the first end-to-end neural network system for Persian pronoun resolution, leveraging pre-trained Transformer models like ParsBERT. Our system jointly optimizes both mention detection and antecedent linking, achieving a 3.37 F1 score improvement over the previous state-of-the-art system (which relied on rule-based and statistical methods) on the Mehr corpus. This significant improvement demonstrates the effectiveness of combining neural networks with linguistic models, potentially marking a significant advancement in Persian pronoun resolution and paving the way for further research in this under-explored area.

A multi-agent deep reinforcement learning-based framework for traffic shaping. The proposed framework offers a key advantage over existing congestion management strategies which is the ability to mitigate hysteresis phenomena. Unlike existing congestion management strategies that focus on breakdown prevention, the proposed framework is extremely effective after breakdown formation. The proposed framework assumes partial connectivity between the automated vehicles which share information. The framework requires a basic level of autonomy defined by one-dimensional longitudinal control. This framework is primarily built using a centralized training, centralized execution multi-agent deep reinforcement learning approach, where longitudinal control is defined by signals of acceleration or deceleration commands which are then executed by all agents uniformly. The model undertaken for training and testing of the framework is based on the well-known Double Deep Q-Learning algorithm which takes the average state of flow within the traffic stream as the model input and outputs actions in the form of acceleration or deceleration values. We demonstrate the ability of the model to shape the state of traffic, mitigate the negative effects of hysteresis, and even improve traffic flow beyond its original level. This paper also identifies the minimum percentage of CAVs required to successfully shape the traffic under an assumption of uniformly distributed CAVs within the loop system. The framework illustrated in this work doesnt just show the theoretical applicability of reinforcement learning to tackle such challenges but also proposes a realistic solution that only requires partial connectivity and continuous monitoring of the average speed of the system, which can be achieved using readily available sensors that measure the speeds of vehicles in reasonable proximity to the CAVs.

Pronoun resolution is a challenging subset of an essential field in natural language processing called coreference resolution. Coreference resolution is about finding all entities in the text that refers to the same real-world entity. This paper presents a hybrid model combining multiple rulebased sieves with a machine-learning sieve for pronouns. For this purpose, seven high-precision rule-based sieves are designed for the Persian language. Then, a random forest classifier links pronouns to the previous partial clusters. The presented method demonstrates exemplary performance using pipeline design and combining the advantages of machine learning and rulebased methods. This method has solved some challenges in end-to-end models. In this paper, the authors develop a Persian coreference corpus called Mehr in the form of 400 documents. This corpus fixes some weaknesses of the previous corpora in the Persian language. Finally, the efficiency of the presented system compared to the earlier model in Persian is reported by evaluating the proposed method on the Mehr and Uppsala test sets.

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.