Question: What baseline models are MPAD compared with
Evidence: We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants.
doc2vec BIBREF37. Doc2vec (or paragraph vector) is an extension of word2vec that learns vectors for documents in a fully unsupervised manner. Document embeddings are then fed to a logistic regression classifier.
CNN BIBREF38. The convolutional neural network architecture, well-known in computer vision, is applied to text. There is one spatial dimension and the word embeddings are used as channels (depth dimensions).
DAN BIBREF39. The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax.
Tree-LSTM BIBREF40 is a generalization of the standard LSTM architecture to constituency and dependency parse trees.
DRNN BIBREF41. Recursive neural networks are stacked and applied to parse trees.
LSTMN BIBREF42 is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations.
C-LSTM BIBREF43 combines convolutional and recurrent neural networks. The region embeddings provided by a CNN are fed to a LSTM.
SPGK BIBREF44 also models documents as word co-occurrence networks. It computes a graph kernel that compares shortest paths extracted from the word co-occurrence networks and then uses a SVM to categorize documents.
WMD BIBREF45 is an application of the well-known Earth Mover's Distance to text. A k-nearest neighbor classifier is used.
S-WMD BIBREF46 is a supervised extension of the Word Mover's Distance.
Semantic-CNN BIBREF47. Here, a CNN is applied to semantic units obtained by clustering words in the embedding space.
LSTM-GRNN BIBREF26 is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector.
HN-ATT BIBREF27 is another hierarchical model, where the same encoder architecture (a bidirectional GRU-RNN) is used for both sentences and documents, with different parameters. A self-attention mechanism is applied to the RNN annotations at each level.
We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants. Doc2vec (Le and Mikolov 2014) is an extension of word2vec that learns vectors for documents in a fully un_x001f_supervised manner. Document embeddings are then fed to a logistic regression classifier. CNN (Kim 2014). 1D convolutional neural network where the word embeddings are used as channels (depth dimensions). DAN (Iyyer et al. 2015). The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax. Tree-LSTM (Tai, Socher, and Manning 2015) is a generalization of the standard LSTM architecture to constituency and dependency parse trees. DRNN (Irsoy and Cardie 2014). Recursive neural networks are stacked and applied to parse trees. LSTMN (Cheng, Dong, and Lapata 2016) is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations. C-LSTM (Zhou et al. 2015) combines convolutional and recurrent neural networks. The region embeddings provided by a CNN are fed to a LSTM. SPGK (Nikolentzos et al. 2017) also models documents as word co-occurrence networks. It computes a graph kernel that compares shortest paths extracted from the word cooccurrence networks and then relies on a SVM. WMD (Kusner et al. 2015) is an application of the well- known Earth Mover's Distance to text. A k-nearest neighbor classifier is used. DiSAN (Shen et al. 2018) uses directional self-attention along with multi-dimensional attention to generate document representations. LSTM-GRNN (Tang, Qin, and Liu 2015) is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector. HN-ATT (Yang et al. 2016) is another hierarchical model, where the same encoder architecture (a bidirectional GRU- RNN) is used for both sentences and documents. Selfattention is applied at each level.
Answer: The baselines are: Doc2vec (Le and Mikolov 2014) ; CNN (Kim 2014);DAN (Iyyer et al. 2015).; Tree-LSTM (Tai, Socher, and Manning 2015);DRNN (Irsoy and Cardie 2014); LSTMN (Cheng, Dong, and Lapata 2016) ; C-LSTM (Zhou et al. 2015); SPGK (Nikolentzos et al. 2017); WMD (Kusner et al. 2015); DiSAN (Shen et al. 2018);LSTM-GRNN (Tang, Qin, and Liu 2015)  and HN-ATT (Yang et al. 2016).