AbstractEncoder-decoder neural networks have been used for many NLP tasks, such as neural machine translation. They have also been applied to constituent parsing by using bracketed tree structures as a target language, translating input sentences into syntactic trees. A more commonly used method to linearize syntactic trees is the shift-reduce system, which uses a sequence of transition-actions to build trees. We empirically investigate the effectiveness of applying the encoder-decoder network to transition-based parsing. On standard benchmarks, our system gives comparable results to the stack LSTM parser for dependency parsing, and significantly better results compared to the aforementioned parser for constituent parsing, which uses bracketed tree formats.