  We address the problem of speech act recognition (SAR) in asynchronous
conversations (forums, emails). Unlike synchronous conversations (e.g.,
meetings, phone), asynchronous domains lack large labeled datasets to train an
effective SAR model. In this paper, we propose methods to effectively leverage
abundant unlabeled conversational data and the available labeled data from
synchronous domains. We carry out our research in three main steps. First, we
introduce a neural architecture based on hierarchical LSTMs and conditional
random fields (CRF) for SAR, and show that our method outperforms existing
methods when trained on in-domain data only. Second, we improve our initial SAR
models by semi-supervised learning in the form of pretrained word embeddings
learned from a large unlabeled conversational corpus. Finally, we employ
adversarial training to improve the results further by leveraging the labeled
data from synchronous domains and by explicitly modeling the distributional
shift in two domains.
