  Dialog act (DA) recognition is a task that has been widely explored over the
years. 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. In this
study, we explore means to generate more informative segment representations,
not only by exploring different network architectures, but also by considering
different token representations, not only at the word level, but also at the
character and functional levels. At the word level, in addition to the commonly
used uncontextualized embeddings, we explore the use of contextualized
representations, which provide information concerning word sense and segment
structure. Character-level tokenization is important to capture
intention-related morphological aspects that cannot be captured at the word
level. Finally, the functional level provides an abstraction from words, which
shifts the focus to the structure of the segment. We also explore approaches to
enrich the segment representation with context information from the history of
the dialog, both in terms of the classifications of the surrounding segments
and the turn-taking history. This kind of information has already been proved
important for the disambiguation of DAs in previous studies. Nevertheless, we
are able to capture additional information by considering a summary of the
dialog history and a wider turn-taking context. By combining the best
approaches at each step, we achieve results that surpass the previous
state-of-the-art on generic DA recognition on both SwDA and MRDA, two of the
most widely explored corpora for the task. Furthermore, by considering both
past and future context, simulating annotation scenario, our approach achieves
a performance similar to that of a human annotator on SwDA and surpasses it on
MRDA.
