722 research outputs found
Neural Machine Translation for Malayalam Paraphrase Generation
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
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
There is an increasing demand for sentiment analysis of text from social
media which are mostly code-mixed. Systems trained on monolingual data fail for
code-mixed data due to the complexity of mixing at different levels of the
text. However, very few resources are available for code-mixed data to create
models specific for this data. Although much research in multilingual and
cross-lingual sentiment analysis has used semi-supervised or unsupervised
methods, supervised methods still performs better. Only a few datasets for
popular languages such as English-Spanish, English-Hindi, and English-Chinese
are available. There are no resources available for Malayalam-English
code-mixed data. This paper presents a new gold standard corpus for sentiment
analysis of code-mixed text in Malayalam-English annotated by voluntary
annotators. This gold standard corpus obtained a Krippendorff's alpha above 0.8
for the dataset. We use this new corpus to provide the benchmark for sentiment
analysis in Malayalam-English code-mixed texts
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