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The paper tackles (constituent) syntactic parsing by mapping this prediction problem to a sequence-to-sequence alignment problem, and then essentially applying a method recently developed in the context of neural machine translation (LSTM-encoder-decoder with an attention mechanism). The resulting parsing model achieves state-of-the-art results when used in the standard supervised set-up (PTB WSJ) and improves further when estimated in a semi-supervised / co-training regime. What I find especially interesting in this paper is that the attention mechanism is crucial for attaining good generalization properties: without using the attention mechanism LSTM achieves very poor results in the supervised setting. This is an interesting observation which may in principle generate future work focusing on refining the attention model (e.g., moving more in a direction of Neural Turing machines of Graves et al.). This is also somewhat surprising that such simple linearization strategy led to state-of-the-art performance.