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.

该研究提出了一种用于波斯语代词消解的端到端神经网络系统，利用预训练的 Transformer 模型如 ParsBERT。我们的系统同时优化了提及检测和先行链路，相比前期依赖基于规则和统计方法的最新系统，在 Mehr 语料库上实现了 3.37 F1 分数的提高。这一显著改进展示了将神经网络与语言模型结合的有效性，可能标志着波斯语代词消解领域的显著进展，并为进一步的研究铺平了道路。

波斯代词消解：利用神经网络和语言模型

Persian Pronoun Resolution: Leveraging Neural Networks and Language  Models

Learning to follow human instructions is a long-pursued goal in artificial
intelligence. The task becomes particularly challenging if no prior knowledge
of the employed language is assumed while relying only on a handful of examples
to learn from. Work in the past has relied on hand-coded components or manually
engineered features to provide strong inductive biases that make learning in
such situations possible. In contrast, here we seek to establish whether this
knowledge can be acquired automatically by a neural network system through a
two phase training procedure: A (slow) offline learning stage where the network
learns about the general structure of the task and a (fast) online adaptation
phase where the network learns the language of a new given speaker. Controlled
experiments show that when the network is exposed to familiar instructions but
containing novel words, the model adapts very efficiently to the new
vocabulary. Moreover, even for human speakers whose language usage can depart
significantly from our artificial training language, our network can still make
use of its automatically acquired inductive bias to learn to follow
instructions more effectively.

通过两阶段训练过程，我们的神经网络系统自动获得归纳偏差来学习遵循人类指令的通用结构，并能够适应新话语中的新单词。