Multilingual Word Embeddings for Low-Resource Languages using Anchors
  and a Chain of Related Languages
Viktor Hangya Silvia Severini Radoslav Ralev Alexander Fraser Hinrich Schütze
Very low-resource languages, having only a few million tokens worth of data,
are not well-supported by multilingual NLP approaches due to poor quality
cross-lingual word representations. Recent work showed that good cross-lingual
performance can be achieved if a source language is related to the low-resource
target language. However, not all language pairs are related. In this paper, we
propose to build multilingual word embeddings (MWEs) via a novel language
chain-based approach, that incorporates intermediate related languages to
bridge the gap between the distant source and target. We build MWEs one
language at a time by starting from the resource rich source and sequentially
adding each language in the chain till we reach the target. We extend a
semi-joint bilingual approach to multiple languages in order to eliminate the
main weakness of previous works, i.e., independently trained monolingual
embeddings, by anchoring the target language around the multilingual space. We
evaluate our method on bilingual lexicon induction for 4 language families,
involving 4 very low-resource (<5M tokens) and 4 moderately low-resource (<50M)
target languages, showing improved performance in both categories.
Additionally, our analysis reveals the importance of good quality embeddings
for intermediate languages as well as the importance of leveraging anchor
points from all languages in the multilingual space.