Dialog Act Classification
11 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Dialog Act Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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CPED
SILICONE Benchmark
Latest papers
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.
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
srsohn/tod-flow
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Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction.
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI
scutcyr/CPED
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Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition
xingbowen714/darer
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Findings (ACL) 2022
To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions.
Sentence encoding for Dialogue Act classification
NathanDuran/Sentence-Encoding-for-DA-Classification
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Natural Language Engineering 2021
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 are often overlooked or underreported within the literature, for example, the number of words to keep in the vocabulary or input sequences.
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.
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.
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.
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 networks (RNN) has shown promising results when applied to the DA classification problem.
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification
Therefore it is a useful technique for tuning ANN models to yield the best performances for natural language processing tasks.
