  Since their inception, embeddings have become a primary ingredient in many
flavours of Natural Language Processing (NLP) tasks supplanting earlier types
of representation. Even though multilingual embeddings have been used for the
increasing number of multilingual tasks, due to the scarcity of parallel
training data, low-resource languages such as Sinhala, tend to focus more on
monolingual embeddings. Then when it comes to the aforementioned multi-lingual
tasks, it is challenging to utilize these monolingual embeddings given that
even if the embedding spaces have a similar geometric arrangement due to an
identical training process, the embeddings of the languages considered are not
aligned. This is solved by the embedding alignment task. Even in this,
high-resource language pairs are in the limelight while low-resource languages
such as Sinhala which is in dire need of help seem to have fallen by the
wayside. In this paper, we try to align Sinhala and English word embedding
spaces based on available alignment techniques and introduce a benchmark for
Sinhala language embedding alignment. In addition to that, to facilitate the
supervised alignment, as an intermediate task, we also introduce
Sinhala-English alignment datasets. These datasets serve as our anchor datasets
for supervised word embedding alignment. Even though we do not obtain results
comparable to the high-resource languages such as French, German, or Chinese,
we believe our work lays the groundwork for more specialized alignment between
English and Sinhala embeddings.
