Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages

• URL: http://arxiv.org/abs/2311.12489v1
• Date: Tue, 21 Nov 2023 09:59:29 GMT
• Title: Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages
• Authors: Viktor Hangya, Silvia Severini, Radoslav Ralev, Alexander Fraser, Hinrich Sch\"utze
• Abstract summary: We propose to build multilingual word embeddings (MWEs) via a novel language chain-based approach. 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 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.
• Score: 54.832599498774464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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