@inproceedings{patro-etal-2019-kgpchamps,
title = "{KGPC}hamps at {S}em{E}val-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.",
author = "Patro, Jasabanta and
Choudhary, Nitin and
Chittora, Kalpit and
Mukherjee, Animesh",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2040",
doi = "10.18653/v1/S19-2040",
pages = "241--246",
abstract = "This paper describes our approach to solve \textit{Semeval task 3: EmoContext}; where, given a textual dialogue i.e. a user utterance along with two turns of context, we have to classify the emotion associated with the utterance as one of the following emotion classes: \textit{Happy, Sad, Angry} or \textit{Others}. To solve this problem, we experiment with different deep learning models ranging from simple bidirectional LSTM (Long and short term memory) model to comparatively complex attention model. We also experiment with word embedding conceptnet along with word embedding generated from bi-directional LSTM taking input characters. We fine-tune different parameters and hyper-parameters associated with each of our models and report the value of evaluating measure i.e. micro precision along with class wise precision, recall and F1-score of each system. We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0.7261. We also report class wise precision, recall, and f1-score of best performing model along with other models that we have experimented with.",
}
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<abstract>This paper describes our approach to solve Semeval task 3: EmoContext; where, given a textual dialogue i.e. a user utterance along with two turns of context, we have to classify the emotion associated with the utterance as one of the following emotion classes: Happy, Sad, Angry or Others. To solve this problem, we experiment with different deep learning models ranging from simple bidirectional LSTM (Long and short term memory) model to comparatively complex attention model. We also experiment with word embedding conceptnet along with word embedding generated from bi-directional LSTM taking input characters. We fine-tune different parameters and hyper-parameters associated with each of our models and report the value of evaluating measure i.e. micro precision along with class wise precision, recall and F1-score of each system. We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0.7261. We also report class wise precision, recall, and f1-score of best performing model along with other models that we have experimented with.</abstract>
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%0 Conference Proceedings
%T KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.
%A Patro, Jasabanta
%A Choudhary, Nitin
%A Chittora, Kalpit
%A Mukherjee, Animesh
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F patro-etal-2019-kgpchamps
%X This paper describes our approach to solve Semeval task 3: EmoContext; where, given a textual dialogue i.e. a user utterance along with two turns of context, we have to classify the emotion associated with the utterance as one of the following emotion classes: Happy, Sad, Angry or Others. To solve this problem, we experiment with different deep learning models ranging from simple bidirectional LSTM (Long and short term memory) model to comparatively complex attention model. We also experiment with word embedding conceptnet along with word embedding generated from bi-directional LSTM taking input characters. We fine-tune different parameters and hyper-parameters associated with each of our models and report the value of evaluating measure i.e. micro precision along with class wise precision, recall and F1-score of each system. We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0.7261. We also report class wise precision, recall, and f1-score of best performing model along with other models that we have experimented with.
%R 10.18653/v1/S19-2040
%U https://aclanthology.org/S19-2040
%U https://doi.org/10.18653/v1/S19-2040
%P 241-246
Markdown (Informal)
[KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.](https://aclanthology.org/S19-2040) (Patro et al., SemEval 2019)
ACL