On Evaluation of Bangla Word Analogies
This addresses the problem of benchmarking and guiding NLP research for Bangla, a low-resource language, but is incremental as it adapts existing evaluation methods.
The paper tackles the lack of a reliable evaluation dataset for Bangla word embeddings by creating a high-quality Bangla word analogy dataset with 16,678 samples and a translated version with 10,594 samples, revealing that current embeddings struggle to achieve high accuracy on these datasets.
This paper presents a high-quality dataset for evaluating the quality of Bangla word embeddings, which is a fundamental task in the field of Natural Language Processing (NLP). Despite being the 7th most-spoken language in the world, Bangla is a low-resource language and popular NLP models fail to perform well. Developing a reliable evaluation test set for Bangla word embeddings are crucial for benchmarking and guiding future research. We provide a Mikolov-style word analogy evaluation set specifically for Bangla, with a sample size of 16678, as well as a translated and curated version of the Mikolov dataset, which contains 10594 samples for cross-lingual research. Our experiments with different state-of-the-art embedding models reveal that Bangla has its own unique characteristics, and current embeddings for Bangla still struggle to achieve high accuracy on both datasets. We suggest that future research should focus on training models with larger datasets and considering the unique morphological characteristics of Bangla. This study represents the first step towards building a reliable NLP system for the Bangla language1.