Detecting Interrogative Utterances with Recurrent Neural Networks

11/03/2015
by   Junyoung Chung, et al.
0

In this paper, we explore different neural network architectures that can predict if a speaker of a given utterance is asking a question or making a statement. We com- pare the outcomes of regularization methods that are popularly used to train deep neural networks and study how different context functions can affect the classification performance. We also compare the efficacy of gated activation functions that are favorably used in recurrent neural networks and study how to combine multimodal inputs. We evaluate our models on two multimodal datasets: MSR-Skype and CALLHOME.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/12/2017

Shifting Mean Activation Towards Zero with Bipolar Activation Functions

We propose a simple extension to the ReLU-family of activation functions...
research
05/03/2015

ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks

In this paper, we propose a deep neural network architecture for object ...
research
03/22/2017

Gate Activation Signal Analysis for Gated Recurrent Neural Networks and Its Correlation with Phoneme Boundaries

In this paper we analyze the gate activation signals inside the gated re...
research
10/04/2014

Explain Images with Multimodal Recurrent Neural Networks

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) ...
research
01/02/2018

Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes

Recurrent neural networks are nowadays successfully used in an abundance...
research
05/16/2018

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

Recent approaches for dialogue act recognition have shown that context f...
research
07/08/2018

Automated labeling of bugs and tickets using attention-based mechanisms in recurrent neural networks

We explore solutions for automated labeling of content in bug trackers a...

Please sign up or login with your details

Forgot password? Click here to reset