588 citations · 1.4k across the 11 of their papers we have counts for
3 papers · 2 filters
Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation
Mikel Artetxe, Gorka Labaka, Iñigo Lopez-Gazpio +1
Following the recent success of word embeddings, it has been argued that there is no such thing as an ideal representation for words, as different models tend to capture divergent…
Unsupervised Statistical Machine Translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre
While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora…
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Mikel Artetxe, Gorka Labaka, Eneko Agirre
Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training. However, thei…