Papers Dialog Act Classification

Hierarchical Fusion for Online Multimodal Dialog Act Classification

We propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances. Existing multimodal DA classification approaches are limited…

TOD-Flow: Modeling the Structure of Task-Oriented Dialogues

Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications. While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitati…

SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks

Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In particul…

CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI

Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an importan…

A Universality-Individuality Integration Model for Dialog Act Classification

Dialog Act (DA) reveals the general intent of the speaker utterance in a conversation. Accurately predicting DAs can greatly facilitate the development of dialog agents. Although researchers have done extensive research …

DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition

The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. In this paper, we put forward a new frame…

Sentence encoding for Dialogue Act classification

In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which…

Privacy Guarantees for De-identifying Text Transformations

Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data. At the same time, there is a clear need for protecting the privacy of the users whose data …

Annotation Process for the Dialog Act Classification of a Taglish E-commerce Q\&A Corpus

With conversational agents or chatbots making up in quantity of replies rather than quality, the need to identify user intent has become a main concern to improve these agents. Dialog act (DA) classification tackles this…

Multi-level Gated Recurrent Neural Network for Dialog Act Classification

In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in g…

Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions

This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional neural networks (CNNs) and conditional ra…

Self-Governing Neural Networks for On-Device Short Text Classification

Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devi…

Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks

Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance improves rapidly when the context is taken i…

JAIST Annotated Corpus of Free Conversation

Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks

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 networ…

Lexico-acoustic Neural-based Models for Dialog Act Classification

Recent works have proposed neural models for dialog act classification in spoken dialogs. However, they have not explored the role and the usefulness of acoustic information. We propose a neural model that processes both…

Using Context Information for Dialog Act Classification in DNN Framework

Previous work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent st…

Neural-based Context Representation Learning for Dialog Act Classification

We explore context representation learning methods in neural-based models for dialog act classification. We propose and compare extensively different methods which combine recurrent neural network architectures and atten…

Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification

Systems based on artificial neural networks (ANNs) have achieved state-of-the-art results in many natural language processing tasks. Although ANNs do not require manually engineered features, ANNs have many hyperparamete…

A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models