  Dialog act identification plays an important role in understanding
conversations. It has been widely applied in many fields such as dialogue
systems, automatic machine translation, automatic speech recognition, and
especially useful in systems with human-computer natural language dialogue
interfaces such as virtual assistants and chatbots. The first step of
identifying dialog act is identifying the boundary of the dialog act in
utterances. In this paper, we focus on segmenting the utterance according to
the dialog act boundaries, i.e. functional segments identification, for
Vietnamese utterances. We investigate carefully functional segment
identification in two approaches: (1) machine learning approach using maximum
entropy (ME) and conditional random fields (CRFs); (2) deep learning approach
using bidirectional Long Short-Term Memory (LSTM) with a CRF layer
(Bi-LSTM-CRF) on two different conversational datasets: (1) Facebook messages
(Message data); (2) transcription from phone conversations (Phone data). To the
best of our knowledge, this is the first work that applies deep learning based
approach to dialog act segmentation. As the results show, deep learning
approach performs appreciably better as to compare with traditional machine
learning approaches. Moreover, it is also the first study that tackles dialog
act and functional segment identification for Vietnamese.
