Antonym-Synonym Classification Based on New Sub-space Embeddings
Table 4: Antonym/Synonym distinction for Urdu language
['[BOLD] Model', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
We verify the language agnostic property of our model by testing its performance for antonym/synonym distinction task on another language. We manually curated a dataset for the Urdu language using linguistic resource and expert verification. It consists of 750 instances with an equal proportion of antonyms and synonyms. We split the dataset into 70% train, 25% test and 5% validation set. Huge improvement in the performance compared with the Direct baseline shows that the proposed model is language-agnostic and has potential to work for other languages provided with the availability of pre-trained embeddings and a minimal set of training seeds.