Recently, large language models (LLMs) enhanced by self-reflection have achieved promising performance on machine transla004 tion. The key idea is guiding LLMs to generate translation with human-like feedback. However, existing self-reflection methods lack effective feedback information, limiting the translation performance. To address this, we introduce a DUAL-REFLECT framework, leveraging the dual learning of translation tasks to provide effective feedback, thereby enhancing the models’ self-reflective abilities and improving translation performance. The application of this method across various translation tasks has proven its effectiveness in improving translation accuracy and eliminating ambiguities, especially in translation tasks with low-resource language pairs.

In the era of large models, low-resource question-answering tasks lag, emphasizing the importance of data augmentation - a key research avenue in natural language processing. The main challenges include leveraging the large model’s internal knowledge for data augmentation, determining which QA data component - the question, passage, or answer - benefits most from augmentation, and retaining consistency in the augmented content without inducing excessive noise. To tackle these, we introduce PQQ, an innovative approach for question data augmentation consisting of Prompt Answer, Question Generation, and Question Filter. Our experiments reveal that ChatGPT underperforms on the experimental data, yet our PQQ method excels beyond existing augmentation strategies. Further, its universal applicability is validated through successful tests on high-resource QA tasks like SQUAD1.1 and TriviaQA.

知识图谱表示学习是自然语言处理的一项关键技术,现有的知识图谱表示研究主要集中在英语、汉语等语言,而低资源语言的知识图谱表示学习研究还处于探索阶段,例如藏语。本文基于前期构建的藏语知识图谱,提出了一种联合胶囊神经网络(JCapsR)的藏语知识图谱表示学习模型。首先,我们使用TransR模型生成藏语知识图谱的结构化信息表示。其次,采用融合多头注意力和关系注意力的Transformer模型表示藏语实体的文本描述信息。最后,采用JCapsR进一步提取三元组在知识图谱语义空间中的关系,将实体文本描述信息和结构化信息融合,得到藏语知识图谱的表示。实验结果表明,相比基线系统,联合胶囊神经网络JCapsR模型提高了藏语知识图谱表示学习的效果,相关研究为其它低资源语言知识图谱表示学习的拓展优化提供了参考借鉴意义。