Abstract: Braille is a fundamental textual medium through which blind and visually impaired people access information, receive education, and participate in social life. Automatic and accurate Chinese-to-Braille translation provides important technical support for promoting information accessibility and information equity. However, several technical challenges remain in automatic Chinese-to-Braille conversion. Traditional rule- and dictionary-based methods rely heavily on manually designed pinyin mappings, Braille word segmentation and concatenation rules, and punctuation conversion rules. Owing to their limited ability to understand deep contextual semantics, these methods tend to achieve relatively low accuracy in scenarios involving polyphone disambiguation, ambiguous sentence structures, and long-distance semantic dependencies. Although general neural machine translation models provide end-to-end sequence transformation capabilities, they are susceptible to insufficient generalization and sequence alignment errors under low-resource Chinese–Braille parallel corpus conditions. Moreover, most existing neural network models employ a unified feedforward structure, making it difficult to handle differentiated linguistic phenomena, including semantic understanding, Braille word segmentation, and symbol generation, in a specialized manner. Consequently, these models may produce insufficiently fluent translations and fail to maintain consistency with Braille rules when processing complex texts. To address these limitations, this paper proposes a Chinese-to-Braille translation model that integrates a pretrained language model with a Mixture-of-Experts (MoE) network, thereby constructing an end-to-end translation architecture that combines contextual semantic understanding with task-specific feature representation. The proposed model adopts an encoder–decoder architecture. In the encoder, a pretrained Bidirectional Encoder Representations from Transformers (BERT) model is employed as the core semantic feature extraction module. Its bidirectional contextual encoding capability is used to improve the identification of Chinese polyphones, ambiguous structures, and long-distance semantic dependencies. An MoE enhancement layer is introduced after the BERT output layer. A learnable gating network computes the routing weights of different experts and dynamically integrates their outputs, thereby improving the model’s ability to process differentiated linguistic information related to semantic features, Braille word segmentation boundaries, punctuation marks, and special symbols. The decoder consists of six stacked Transformer Decoder layers. Conditioned on the enhanced semantic representations produced by the encoder, the decoder combines target Braille sequence embeddings with positional encoding information and generates the target Braille symbol sequence autoregressively. To support model training and evaluation, a Chinese–Braille parallel corpus was compiled in collaboration with a school for blind students. The corpus covers several common text categories, including news reports, literary works, science education materials, public service documents, and daily communication texts. Data cleaning, duplicate removal, and Braille symbol normalization were performed to improve the consistency and usability of the corpus. The model was evaluated using the Bilingual Evaluation Understudy (BLEU), Character Error Rate (CER), Character Accuracy (CharAcc), and Exact Match (EM). These metrics respectively measure overall n-gram consistency, character-level editing errors, character-level correctness, and complete sequence consistency. Under the same training data, data split, preprocessing procedure, and evaluation settings, the proposed model achieved BLEU, CER, CharAcc, and EM scores of 98.15%, 1.46%, 98.54%, and 63.99%, respectively, on the self-built test set. Compared with the standard Transformer baseline under the same experimental conditions, the proposed model improved BLEU by 11.64 percentage points and reduced CER by 17.24 percentage points. The ablation results showed that introducing the MoE network alone increased BLEU from 86.51% to 92.12% and reduced CER from 18.70% to 10.17%. Introducing the BERT encoder alone increased BLEU to 97.02% and reduced CER to 1.84%. When the BERT encoder and the MoE network were used together, the model achieved the best results among the ablation models evaluated in this study, indicating that the two components provide complementary benefits. A subjective experiment involving visually impaired users was also conducted to assess the practical reading quality of the generated Braille texts. The results showed that the translation method had a significant effect on Braille reading scores. The proposed model received relatively high ratings for reading fluency, content comprehensibility, and semantic accuracy across daily-life, science education, and literary texts, while its rating distributions were relatively concentrated. The combined results of automatic evaluation, ablation experiments, and the user study indicate that BERT-based semantic encoding enhances the contextual representation of Chinese inputs, whereas the MoE mechanism dynamically integrates expert outputs according to input features. Their combination helps reduce character-level Braille errors and improve the exact-match rate of complete sequences. At a medium model scale, the proposed method achieves favorable Chinese-to-Braille translation performance and can provide technical support for Braille publishing, accessible conversion of digital reading materials, intelligent assistive devices, and public Braille information services.

摘要： 盲文是视障群体获取信息、接受教育、参与社会生活的基础性文字载体，中文到盲文的自动精准翻译是推进信息无障碍、保障信息公平的关键技术支撑。当前中文‑盲文自动转换领域仍面临多重技术瓶颈：传统基于规则与词典的方法高度依赖人工编写的拼音映射以及分词连写与标点转换规则，缺乏深层上下文语义理解能力，在多音字消歧、歧义句式判定、长句语义建模等场景中准确率偏低；通用神经机器翻译模型虽具备端到端建模能力，但在中文‑盲文低资源平行语料条件下易出现泛化不足、序列对齐偏差等问题；现有神经网络模型多采用统一前馈结构，难以对语义理解、盲文分词、符号生成等差异化语言现象进行分工建模，导致复杂文本翻译流畅度不足、规则一致性差。为突破上述局限，本文提出一种融合预训练语言模型与混合专家网络的中文到盲文翻译模型，构建兼顾语义理解深度与任务专门化能力的端到端翻译架构。模型以编码器‑解码器为基础框架。编码器采用预训练BERT模型作为核心语义提取模块，充分利用其双向上下文编码能力，提升对中文多音字、歧义结构、长距离语义依赖的捕捉能力；在BERT输出层后引入混合专家网络增强层，通过可学习门控网络计算不同专家的路由权重并动态融合专家输出，从而增强模型对语义特征、盲文分词边界、标点与特殊符号等差异化语言信息的处理能力。解码器由6层Transformer Decoder堆叠构成，以编码器输出的增强语义表示为条件，结合目标盲文序列嵌入与位置编码信息，通过自回归方式逐步生成目标盲文符号序列。为支撑模型训练与评估，本文与盲校合作整理构建了中文-盲文平行语料库，语料内容涵盖新闻资讯、文学作品、科普教育、公共服务文件和日常交流文本等常见类型，并通过数据清洗、重复样本去除和盲文符号规范化等步骤提升语料的一致性和可用性。实验采用双语评估替补（BLEU）、字符错误率（CER）、字符级准确率（CharAcc）和完全匹配率（EM）等指标，分别从整体n-gram一致性、字符级编辑错误、字符级正确程度和序列完全一致性等方面评价模型性能。在相同训练数据、数据划分、预处理流程和评价设置下，本文模型在自建测试集上的BLEU、CER、CharAcc和EM分别为98.15%、1.46%、98.54%和63.99%。与标准Transformer基线模型相比，本文模型的BLEU提高11.64个百分点，CER降低17.24个百分点。消融实验结果表明，单独引入混合专家网络后，BLEU由86.51%提高至92.12%，CER由18.70%降低至10.17%；单独引入BERT编码器后，BLEU达到97.02%，CER降低至1.84%。当同时使用BERT编码器与混合专家网络时，模型在本文设置的消融模型中取得最佳结果，说明二者具有一定的互补作用。面向视障用户的主观实验结果表明，翻译方法对盲文阅读评分具有显著影响，本文模型在日常生活、科普教育和文学艺术三类文本的阅读流畅度、内容辨识度和语义准确性方面均取得较高评分，且评分分布较为集中。综合自动评价、消融实验和用户实验结果，BERT语义编码能够增强中文输入的上下文表示，混合专家机制能够依据输入特征动态融合专家输出，二者结合有助于降低盲文字符级错误并提高完整序列匹配率。本文方法在中等模型规模下取得了较好的中文到盲文翻译性能，可为盲文出版、数字读物无障碍转换、智能助盲终端和公共信息盲文服务等应用提供技术支持。