text: This paper proposed to jointly train a multilingual skip-gram model and a cross-lingual sentence similarity model to construct sentence embeddings.  They used cross-lingual classification tasks for evaluation.  This idea is fairly simple but interesting.  Their results on some language pairs showed that the joint training is effective  (results on table 1 showed that sent-LSTM worked best with dim=128).  The downside of this paper is that their results could not outperform state-of-the-art results.  Some detailed comments: -	The authors should weaken some of the statements, e.g. ‘since our multilingual skip-gram and cross-lingual sentence similarity models are trained jointly, they can inform each other through the shared word embedding layer and promote the compositionality of learned word embeddings at training time’.  Actually, there are no experimental results and evidences in this paper supporting this statement. -	I don’t  see that ‘Amenable to Multi-task modeling’ is a contribution of this paper.  The authors should report additional experimental results to prove this statement.