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We discuss Feature Latent Semantic Analysis (FLSA), an extension to Latent Semantic Analysis (LSA). LSA is a statistical method that is ordinar- ily trained on words only; FLSA adds to …...

Instant Messaging (IM) chat sessions are real-time, text-based conversations which can be analyzed using dialogue-act mod- els. Dialogue acts represent the seman- tic information of an utterance, however, messages must …...

We explore the task of automatically classify- ing dialogue acts in 1-on-1 online chat forums, an increasingly popular means of providing customer service. In particular, we investi- gate the effectiveness …...

Adjacency pair recognition, a necessary com- ponent of discussion thread reconstruction, is the task of recognizing reply-to relations be- tween pairs of discussion turns. Previously, dialogue act classification and metadata-based …...

AbstractIn this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number …...

AbstractThis paper introduces a novel training/decoding strategy for sequence labeling. Instead of greedily choosing a label at each time step, and using it for the next prediction, we retain the …...

The ability to model and automatically detect dialogue act is an important step toward understanding spontaneous speech and Instant Messages. However, it has been difficult to infer a dialogue act …...

AbstractRecent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness …...

AbstractOur goal is to develop an intelligent assistant to support users explore data via visualizations. We have collected a new corpus of conversations, CHICAGO-CRIME-VIS, geared towards supporting data visualization exploration, …...

AbstractWe present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems. Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, …...

AbstractThe task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively. But these studies limit themselves to text. Non-verbal features (change of tone, facial …...

AbstractDialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level …...

AbstractThe #MeToo movement on social media platforms initiated discussions over several facets of sexual harassment in our society. Prior work by the NLP community for automated identification of the narratives …...

AbstractDialogue Act Classification (DAC) that determines the communicative intention of an utterance has been investigated widely over the years as a standalone task. But the emotional state of the speaker …...

AbstractWe propose a speaker clustering model for textual dialogues, which groups the utterances of a multi-party dialogue without speaker annotations, so that the actual speakers are identical inside each cluster. …...