The rapid expansion of texts' volume and diversity presents formidable
challenges in multi-domain settings. These challenges are also visible in the
Persian name entity recognition (NER) settings. Traditional approaches, either
employing a unified model for multiple domains or individual models for each
domain, frequently pose significant limitations. Single models often struggle
to capture the nuances of diverse domains, while utilizing multiple large
models can lead to resource constraints, rendering the training of a model for
each domain virtually impractical. Therefore, this paper introduces a novel
approach composed of one core model with multiple sets of domain-specific
parameters. We utilize techniques such as prompt tuning and adapters, combined
with the incorporation of additional layers, to add parameters that we can
train for the specific domains. This enables the model to perform comparably to
individual models for each domain. Experimental results on different formal and
informal datasets show that by employing these added parameters, the proposed
model significantly surpasses existing practical models in performance.
Remarkably, the proposed model requires only one instance for training and
storage, yet achieves outstanding results across all domains, even surpassing
the state-of-the-art in some. Moreover, we analyze each adaptation strategy,
delineating its strengths, weaknesses, and optimal hyper-parameters for the
Persian NER settings. Finally, we introduce a document-based domain detection
pipeline tailored for scenarios with unknown text domains, enhancing the
adaptability and practicality of this paper in real-world applications.

本研究提出一种使用核心模型和多组领域特定参数的新方法，通过采用提示调优和适配器技术，结合额外层次的参数训练，使得模型能够在各个领域表现出色，甚至在某些领域超越现有模型。此外，我们还分析了各自适应策略的优势、劣势和最佳超参数，最后引入了一种基于文档的领域检测流程，提高了本研究在未知文本领域情景下的适应性和实用性。

多 BERT：利用适配器和提示调整进行低资源多领域适应

Multi-BERT: Leveraging Adapters and Prompt Tuning for Low-Resource  Multi-Domain Adaptation

Dialogue state tracking (DST) is an essential sub-task for task-oriented
dialogue systems. Recent work has focused on deep neural models for DST.
However, the neural models require a large dataset for training. Furthermore,
applying them to another domain needs a new dataset because the neural models
are generally trained to imitate the given dataset. In this paper, we propose
Schema Encoding for Transferable Dialogue State Tracking (SETDST), which is a
neural DST method for effective transfer to new domains. Transferable DST could
assist developments of dialogue systems even with few dataset on target
domains. We use a schema encoder not just to imitate the dataset but to
comprehend the schema of the dataset. We aim to transfer the model to new
domains by encoding new schemas and using them for DST on multi-domain
settings. As a result, SET-DST improved the joint accuracy by 1.46 points on
MultiWOZ 2.1.

该论文提出了一种名为 SETDST 的基于神经网络的对话状态跟踪方法，通过对数据集的 schema 进行编码实现对新领域的有效迁移学习，进而提高多领域设置下的对话系统性能。