This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphrases using both automated metrics, such as BLEU, METEOR, and cosine similarity, as well as human annotation. Our findings suggest that automated evaluation measures may not be fully appropriate for Malayalam, as they do not consistently align with human judgment. This discrepancy underscores the need for more nuanced paraphrase evaluation approaches especially for highly agglutinative languages.

翻译：本研究探索了四种在马拉雅拉姆语中生成复述的方法，利用英语复述的现有资源及预训练的神经机器翻译（NMT）模型。我们通过自动评估指标（如BLEU、METEOR和余弦相似度）以及人工标注对生成的复述进行了评估。研究结果表明，自动评估方法可能不完全适用于马拉雅拉姆语，因为其评估结果与人工判断并不一致。这种差异凸显了针对高度黏着语言开发更精细复述评估方法的必要性。