You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Common to these two types of models is the fact that at each time step t in the decoding phase, both approaches first take as input the hidden state \mbox{\boldmath{$h$}}_{t} at the top layer of a stacking LSTM. The goal is then to derive a context vector \mbox{\boldmath{$c$}}_{t} that captures relevant source-side information to help predict the current target word y_{t}. While these models differ in how the context vector \mbox{\boldmath{$c$}}_{t} is derived, they share the same subsequent steps. ?) used an RNN with the standard hidden unit for the decoder and aconvolutional neural network for encoding the source sentence representation. Onthe other hand, both ?) and ?) stackedmultiple layers of an RNN with a Long Short-Term Memory (LSTM) hidden unit forboth the encoder and the decoder. ?), ?), and?) all adopted a different version of the RNN with anLSTM-inspired hidden unit, the gated recurrent unit (GRU), for bothcomponents.444They all used a single RNN layer except for the latter twoworks which utilized a bidirectional RNN for the encoder. In more detail, one can parameterize the probability of decoding each word y_{j} as:p\left(y_{j}|y_{<j},\mbox{\boldmath{$s$}}\right)=\operatorname{softmax}\left(g\left(\mbox{\boldmath{$h$}}_{j}\right)\right)(2)with g being the transformation function that outputs a vocabulary-sizedvector.555One can provide g with other inputs such as the currentlypredicted word y_{j} as in [Bahdanau et al., 2015]. Here, \mbox{\boldmath{$h$}}_{j} is the RNN hiddenunit, abstractly computed as:\mbox{\boldmath{$h$}}_{j}=f(\mbox{\boldmath{$h$}}_{j-1},\mbox{\boldmath{$s$}}),(3)where f computes the current hidden state given the previous hidden state andcan be either a vanilla RNN unit, a GRU, or an LSTM unit. In [Kalchbrenner and Blunsom, 2013, Sutskever et al., 2014, Cho et al., 2014, Luong et al., 2015], the source representation s is only used once to initialize the decoder hidden state. On the other hand, in [Bahdanau et al., 2015, Jean et al., 2015] and this work, s, in fact, implies a set of source hidden states which are consulted throughout the entire course of the translation process. Such an approach is referred to as an attention mechanism, which we will discuss next.

Question: What kinds of RNN architectures were used for the decoder in various prior research?
Previous work included vanilla RNN, LSTM and GRU in the decoder architecture. as Sutskever and Luon stacked multiple layers of RNN with LSTM hidden unit for the decoder and encoder. and Cho, Bahdanau and Jeal all adopted GRU.