text: 264,60,1139477376864260097,892059194240532480,Mikel Artetxe,"1/4 New @ACL2019_Italy paper by our awesome student @aormazabalo on the limitations of cross-lingual word embedding mappings (w/ @glabaka, @Aitor57, @eagirre & myself) <LINK> Thread 👇 <LINK> 2/4 It was shown that the isomorphism assumption in cross-lingual embeddings doesn't fully hold. But is this a consequence of aligning separately trained embeddings (so an inherent limitation of mapping methods)? Or a more general issue caused by divergences across languages? 3/4 We try to answer this question by comparing mapping to joint learning on parallel corpora. In these ideal conditions, joint learning yields more isomorphic embeddings, is less sensitive to hubness, and better at bilingual lexicon induction, especially for distant languages. 4/4 Mapping methods still have the advantage of requiring less (or no) supervision, but this shows that they also have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.",https://arxiv.org/abs/1906.05407,"Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations. While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross-lingual embeddings. So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces. We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction. We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal. ",Analyzing the Limitations of Cross-lingual Word Embedding Mappings,4,"['1/4 New @ACL2019_Italy paper by our awesome student @aormazabalo on the limitations of cross-lingual word embedding mappings (w/ @glabaka, @Aitor57, @eagirre &amp; myself) <LINK>\n\nThread 👇 <LINK>', ""2/4 It was shown that the isomorphism assumption in cross-lingual embeddings doesn't fully hold. But is this a consequence of aligning separately trained embeddings (so an inherent limitation of mapping methods)? Or a more general issue caused by divergences across languages?"", '3/4 We try to answer this question by comparing mapping to joint learning on parallel corpora. In these ideal conditions, joint learning yields more isomorphic embeddings, is less sensitive to hubness, and better at bilingual lexicon induction, especially for distant languages.', '4/4 Mapping methods still have the advantage of requiring less (or no) supervision, but this shows that they also have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.']",19,06,992