We compare the proposed DAH-CRF model incorporating utterance-level topic labels extracted by LDA (denoted as DAH-CRF+LDA utt ) against five strong baselines and two variants of our own models: JAS 5 : A generative joint, additive, sequential model of topics and speech acts in patient-doctor communication (Wallace et al., 2013); DRLM-Cond 6 : A latent variable recurrent neural network for DA classification (Ji et al., 2016); Bi-LSTM-CRF 7 : A hierarchical Bi-LSTM with a CRF to classify DAs (Kumar et al., 2018); CRF-ASN: An attentive structured network with a CRF for DA classification (Chen et al., 2018); SelfAtt-CRF: A hierarchical Bi-GRU with selfattention and CRF (Raheja and Tetreault, 2019); DAH-CRF+MANUAL conv : Use the manually annotated conversation-level topic labels (i.e., each utterance of the conversation shares the same 5 https://github.com/bwallace/JAS 6 https://github.com/jiyfeng/drlm 7 https://github.com/YanWenqiang/HBLSTM-CRF  topic) for DAH-CRF model training rather than the topic labels automatically acquired from LDA; DAH-CRF+LDA conv : Use conversation-level topic labels automatically acquired from LDA for DAH-CRF model training.
they applied structured attention network to the CRF layer in order to model contextual utterances and corresponding DAs together.