Abstract
Cognates are variants of the same lexical form across different languages;
for example 'fonema' in Spanish and 'phoneme' in English are cognates, both of
which mean 'a unit of sound'. The task of automatic detection of cognates among
any two languages can help downstream NLP tasks such as Cross-lingual
Information Retrieval, Computational Phylogenetics, and Machine Translation. In
this paper, we demonstrate the use of cross-lingual word embeddings for
detecting cognates among fourteen Indian Languages. Our approach introduces the
use of context from a knowledge graph to generate improved feature
representations for cognate detection. We, then, evaluate the impact of our
cognate detection mechanism on neural machine translation (NMT), as a
downstream task. We evaluate our methods to detect cognates on a challenging
dataset of twelve Indian languages, namely, Sanskrit, Hindi, Assamese, Oriya,
Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam.
Additionally, we create evaluation datasets for two more Indian languages,
Konkani and Nepali. We observe an improvement of up to 18% points, in terms of
F-score, for cognate detection. Furthermore, we observe that cognates extracted
using our method help improve NMT quality by up to 2.76 BLEU. We also release
our code, newly constructed datasets and cross-lingual models publicly.