Impact of Parallel Corpus Size on Cross-Lingual Word Embedding Quality in Low-Resource Languages

We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from Research goal: What is the impact of varying the size of parallel corpora (e.g., 100 vs. 1,000 sentence pairs) on the quality of contextualized cross-lingual word embeddings when evaluated on the XNLI benchmark for low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.3/10.