Neural Machine Translation for Malayalam Paraphrase Generation
This addresses the problem of evaluating paraphrases for researchers and practitioners in low-resource, agglutinative languages like Malayalam, but it is incremental as it applies existing methods to a new domain.
The study tackled paraphrase generation in Malayalam using Neural Machine Translation methods, finding that automated metrics like BLEU and METEOR do not consistently align with human judgment, highlighting a need for better evaluation approaches.
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.