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Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT
We aim to investigate whether UNMT approaches with self-supervised pre-training are robust to word-order divergence between language pairs.
Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study
In this paper, we show that initializing the embedding layer of UNMT models with cross-lingual embeddings shows significant improvements in BLEU score over existing approaches with embeddings randomly initialized.
Scrambled Translation Problem: A Problem of Denoising UNMT
We hypothesise that the reason behind \textit{scrambled translation problem} is 'shuffling noise' which is introduced in every input sentence as a denoising strategy.
Meaningless yet meaningful: Morphology grounded subword-level NMT
We explore the use of two independent subsystems Byte Pair Encoding (BPE) and Morfessor as basic units for subword-level neural machine translation (NMT).
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