8 research outputs found
Recurrent Polynomial Network for Dialogue State Tracking
Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states as a dialogue progresses. Recent studies on constrained Markov Bayesian polynomial (CMBP) framework take the first step towards bridging the gap between rule-based and statistical approaches for DST. In this paper, the gap is further bridged by a novel framework -- recurrent polynomial network (RPN). RPN's unique structure enables the framework to have all the advantages of CMBP including efficiency, portability and interpretability. Additionally, RPN achieves more properties of statistical approaches than CMBP. RPN was evaluated on the data corpora of the second and the third Dialog State Tracking Challenge (DSTC-2/3). Experiments showed that RPN can significantly outperform both traditional rule-based approaches and statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with a lot richer features, RPN is also competitive
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
Introduction to the Special Issue on Dialogue State Tracking
This short article introduces the Special Issue on Dialogue State Tracking
Training Input-Output Recurrent Neural Networks through Spectral Methods
We consider the problem of training input-output recurrent neural networks
(RNN) for sequence labeling tasks. We propose a novel spectral approach for
learning the network parameters. It is based on decomposition of the
cross-moment tensor between the output and a non-linear transformation of the
input, based on score functions. We guarantee consistent learning with
polynomial sample and computational complexity under transparent conditions
such as non-degeneracy of model parameters, polynomial activations for the
neurons, and a Markovian evolution of the input sequence. We also extend our
results to Bidirectional RNN which uses both previous and future information to
output the label at each time point, and is employed in many NLP tasks such as
POS tagging
Training Input-Output Recurrent Neural Networks through Spectral Methods
We consider the problem of training input-output recurrent neural networks (RNN) for sequence labeling tasks. We propose a novel spectral approach for learning the network parameters. It is based on decomposition of the cross-moment tensor between the output and a non-linear transformation of the input, based on score functions. We guarantee consistent learning with polynomial sample and computational complexity under transparent conditions such as non-degeneracy of model parameters, polynomial activations for the neurons, and a Markovian evolution of the input sequence. We also extend our results to Bidirectional RNN which uses both previous and future information to output the label at each time point, and is employed in many NLP tasks such as POS tagging