EmotionX-JTML: Detecting emotions with Attention

This paper addresses the problem of automatic recognition of emotions in conversational text datasets for the EmotionX challenge. Emotion is a human characteristic expressed through several modalities (e.g., auditory, visual, tactile). Trying to detect emotions only from the text becomes a difficult task even for humans. This paper evaluates several neural architectures based on Attention Models, which allow extracting relevant parts of the context within a conversation to identify the emotion associated with each utterance. Empirical results in the validation datasets demonstrate the effectiveness of the approach compared to the reference models for some instances, and other cases show better results with simpler models.

[1]  Von-Wun Soo,et al.  Towards Text-based Emotion Detection A Survey and Possible Improvements , 2009, 2009 International Conference on Information Management and Engineering.

[2]  Dan Jurafsky,et al.  Dialog Act Modeling for Conversational Speech , 1998 .

[3]  Bowen Zhou,et al.  ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs , 2015, TACL.

[4]  Phil Blunsom,et al.  Reasoning about Entailment with Neural Attention , 2015, ICLR.

[5]  Lun-Wei Ku,et al.  EmotionLines: An Emotion Corpus of Multi-Party Conversations , 2018, LREC.

[6]  Matthias Zimmermann,et al.  Joint segmentation and classification of dialog acts using conditional random fields , 2009, INTERSPEECH.

[7]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[8]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[9]  P. Ekman,et al.  DIFFERENCES Universals and Cultural Differences in the Judgments of Facial Expressions of Emotion , 2004 .

[10]  Gholamreza Haffari,et al.  A Latent Variable Recurrent Neural Network for Discourse Relation Language Models , 2016, ArXiv.

[11]  J. Averill.  A CONSTRUCTIVIST VIEW OF EMOTION , 1980 .

[12]  Matthew Henderson,et al.  Discriminative spoken language understanding using word confusion networks , 2012, 2012 IEEE Spoken Language Technology Workshop (SLT).

[13]  Jason Weston,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[14]  Xiaodong He,et al.  Character-Level Question Answering with Attention , 2016, EMNLP.

[15]  Hung-yi Lee,et al.  Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection , 2016, INTERSPEECH.

[16]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[17]  Yoshua Bengio,et al.  Attention-Based Models for Speech Recognition , 2015, NIPS.

[18]  Ngoc Thang Vu,et al.  Neural-based Context Representation Learning for Dialog Act Classification , 2017, SIGDIAL Conference.

[19]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[20]  Lun-Wei Ku,et al.  Sensing Emotions in Text Messages: An Application and Deployment Study of EmotionPush , 2016, COLING.

[21]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[22]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[23]  Phil Blunsom,et al.  Recurrent Convolutional Neural Networks for Discourse Compositionality , 2013, CVSM@ACL.

[24]  Heike Adel,et al.  Exploring Different Dimensions of Attention for Uncertainty Detection , 2016, EACL.

[25]  Franck Dernoncourt,et al.  Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks , 2016, NAACL.