Temporal Knowledge Graph Reasoning (TKGR) aims to predict future facts based on historical data. Current mainstream models primarily use embedding techniques, which predict missing facts by representing entities and relations as low-dimensional vectors. However, these models often consider only the structural information of individual entities and relations, overlooking the broader structure of the entire TKG. To address these limitations, we propose a novel model called Relation Logical Reasoning and Relation-aware Entity Encoding (RLEE), drawing inspiration from attention mechanisms and logical rule-based techniques. RLEE introduces a two-layer representation of the TKG: an entity layer and a relation layer. At the relation layer, we extract relation paths to mine potential logical correlations between different relations, learning relation embeddings through a process of relation logical reasoning. At the entity layer, we use the relation-aware attention mechanism to learn the entity embeddings specific to the predicted query relations. These learned relation and entity embeddings are then used to predict facts at future timestamps. When evaluated on five commonly used public datasets, RLEE consistently outperforms state-of-the-art baselines.

Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Language Models such as ChatGPT holds potential for innovation in this field. This study examines ChatGPT’s capabilities in English-Chinese poetry translation tasks, utilizing targeted prompts and small sample scenarios to ascertain optimal performance. Despite promising outcomes, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. To address these shortcomings, we propose an Explanation-Assisted Poetry Machine Translation (EAPMT) method, which leverages monolingual poetry explanation as a guiding information for the translation process. Furthermore, we refine existing evaluation criteria to better suit the nuances of modern poetry translation. We engaged a panel of professional poets for assessments, complemented evaluations by using GPT-4. The results from both human and machine evaluations demonstrate that our EAPMT method outperforms traditional translation methods of ChatGPT and the existing online systems. This paper validates the efficacy of our method and contributes a novel perspective to machine-assisted literary translation.

“在互联网在线医疗领域,由于大多数患者缺乏医学培训,以及不同学科病理特征的复杂性,医患对话文本中的医学命名实体呈现出长且多词的句法特点,给命名实体识别算法提出了新的挑战。 为解决这一问题,本研究融合多个不同粒度的扩张卷积机制,构建了Flat-Lattice-CNN模型。 该模型不仅考虑字符和词语的语义信息以及它们的绝对和相对位置信息,还提取跨越不同距离的多个字符/词语的共现依存关系特征,以此提高医学长命名实体的识别精度。 实验结果表明,本文提出的模型在所评估数据集的命名实体识别任务上有普遍性的性能提升,尤其是在以长实体为主的中文医疗数据集CTDD上,该模型的F 1值提升了约2%,具有更优的表现。”

The application of machine translation in the field of poetry has always presented significant challenges. Conventional machine translation techniques are inadequate for capturing and translating the unique style of poetry. The absence of a parallel poetry corpus and the distinctive structure of poetry further restrict the effectiveness of traditional methods. This paper introduces a zero-shot method that is capable of translating poetry style without the need for a large-scale training corpus. Specifically, we treat poetry translation as a standard machine translation problem and subsequently inject the poetry style upon completion of the translation process. Our injection model only requires back-translation and easily obtainable monolingual data, making it a low-cost solution. We conducted experiments on three translation directions and presented automatic and human evaluations, demonstrating that our proposed method outperforms existing online systems and other competitive baselines. These results validate the feasibility and potential of our proposed approach and provide new prospects for poetry translation.