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Social coding platforms, such as GitHub, serve as laboratories for studying collaborative problem solving in open source software development; a key feature is their ability to support issue reporting which is used by teams to discuss tasks and ideas.
The identification of Dialogue Act’s (DA) is an important aspect in determining the meaning of an utterance for many applications that require natural language understanding, and recent work using recurrent neural networks (RNN) has shown promising results when applied to the DA classification problem.
Dialogue Act recognition associate dialogue acts (i. e., semantic labels) to utterances in a conversation.
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.
Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications.
Ranked #1 on Dialogue Act Classification on Switchboard corpus
Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks.
Ranked #2 on Dialogue Act Classification on Switchboard corpus
Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels.
CRF models the conditional probability of the target DA label sequence given the input utterance sequence.