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Towards Learning Through Open-Domain Dialog
The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to them.
Automatic Recognition of the General-Purpose Communicative Functions defined by the ISO 24617-2 Standard for Dialog Act Annotation
We explore the recognition of general-purpose communicative functions in the DialogBank, which is a reference set of dialogs annotated according to this standard.
Hierarchical Multi-Label Dialog Act Recognition on Spanish Data
Concerning the single-label classification problem posed by the top level, we show that the conclusions drawn on English data also hold on Spanish data.
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations
Recently, most approaches to the task explored different DNN architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention.
A Study on Dialog Act Recognition using Character-Level Tokenization
In both cases, the experiments not only show that character-level tokenization leads to better performance than the typical word-level approaches, but also that both approaches are able to capture complementary information.
Assessing User Expertise in Spoken Dialog System Interactions
Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques.
Mapping the Dialog Act Annotations of the LEGO Corpus into the Communicative Functions of ISO 24617-2
Furthermore, these are dialogs from a widely explored corpus for dialog related tasks.
The Influence of Context on Dialogue Act Recognition
We performed experiments on the widely explored Switchboard corpus, as well as on data annotated according to the recent ISO 24617-2 standard.
