Neural Machine Translation for Malayalam Paraphrase Generation
Christeena Varghese Sergey Koshelev Ivan P. Yamshchikov
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.