“反馈评语生成任务旨在为语言学习者的产出提供纠偏及解释性的评价,促进学习者写作能力的发展。现有研究主要聚焦于单语的反馈评语生成,如为英语学习者提供英文反馈评语,但这忽略了非母语学习者可能面临的理解障碍问题,尤其当评语中存在陌生的语言知识时。因此,本文提出跨语言反馈评语生成任务(CLFCG),目的是为语言学习者生成母语的反馈评语。本研究构建了首个英甭中跨语言反馈评语生成数据集,该数据集包含英语学习者产出的语句与相应的中文反馈评语,并探索了基于流水线的预训练语言模型引导增强生成方法,将修正编辑、线索词语和语法术语等作为输入的附加信息,引导和提示生成模型。实验结果表明,附加引导信息的预训练语言模型流水线方法在自动评估(BLEU:50.32)与人工评估(Precision:62.84)上表现良好。本文对实验结果进行了深入分析,以期为跨语言反馈评语生成任务提供更多见解。”

“Analyzing long and complicated sentences has always been a priority and challenge in Englishlearning. In order to conduct the parse of these sentences for Chinese English as Second Lan-guage (ESL) learners, we design the English Sentence Pattern Structure (ESPS) based on theSentence Diagramming theory. Then, we automatically construct the English Sentence PatternStructure Treebank (ESPST) through the method of rule conversion based on constituency struc-ture and evaluate the conversion results. In addition, we set up two comparative experiments,using trained parser and large language models (LLMs). The results prove that the rule-basedconversion approach is effective.”