  Classifying the general intent of the user utterance in a conversation, also
known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or
request for an opinion, is a key step in Natural Language Understanding (NLU)
for conversational agents. While DA classification has been extensively studied
in human-human conversations, it has not been sufficiently explored for the
emerging open-domain automated conversational agents. Moreover, despite
significant advances in utterance-level DA classification, full understanding
of dialogue utterances requires conversational context. Another challenge is
the lack of available labeled data for open-domain human-machine conversations.
To address these problems, we propose a novel method, CDAC (Contextual Dialogue
Act Classifier), a simple yet effective deep learning approach for contextual
dialogue act classification. Specifically, we use transfer learning to adapt
models trained on human-human conversations to predict dialogue acts in
human-machine dialogues. To investigate the effectiveness of our method, we
train our model on the well-known Switchboard human-human dialogue dataset, and
fine-tune it for predicting dialogue acts in human-machine conversation data,
collected as part of the Amazon Alexa Prize 2018 competition. The results show
that the CDAC model outperforms an utterance-level state of the art baseline by
8.0% on the Switchboard dataset, and is comparable to the latest reported
state-of-the-art contextual DA classification results. Furthermore, our results
show that fine-tuning the CDAC model on a small sample of manually labeled
human-machine conversations allows CDAC to more accurately predict dialogue
acts in real users' conversations, suggesting a promising direction for future
improvements.
