pwc-archive/papers-with-abstracts huggingface dataset:
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection
Authors: Joan Xiao
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Transfer Learning, Word Embeddings

This paper describes our transfer learning-based approach to contextual emotion detection as part of SemEval-2019 Task 3. We experiment with transfer learning using pre-trained language models (ULMFiT, OpenAI GPT, and BERT) and fine-tune them on this task. We also train a deep learning model from scratch using pre-trained word embeddings and BiLSTM architecture with attention mechanism. The ensembled model achieves competitive result, ranking ninth out of 165 teams. The result reveals that ULMFiT performs best due to its superior fine-tuning techniques. We propose improvements for future work.
GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach
Authors: Dumitru Bogdan
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

In this paper, we describe our participation in SemEval-2019 Task 3: EmoContext - A Shared Task on Contextual Emotion Detection in Text. We propose a three layer model with a generic, multi-purpose approach that without any task specific optimizations achieve competitive results (f1 score of 0.7096) in the EmoContext task. We describe our development strategy in detail along with an exposition of our results.
GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus
Authors: Shabnam Tafreshi, Mona Diab
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Word Embeddings

In this paper we present an emotion classifier models that submitted to the SemEval-2019 Task 3 : \textit{EmoContext}. Our approach is a Gated Recurrent Neural Network (GRU) model with attention layer is bootstrapped with contextual information and trained with a multigenre corpus, which is combination of several popular emotional data sets. We utilize different word embeddings to empirically select the most suited embedding to represent our features. Our aim is to build a robust emotion classifier that can generalize emotion detection, which is to learn emotion cues in a noisy training environment. To fulfill this aim we train our model with a multigenre emotion corpus, this way we leverage from having more training set. We achieved overall {\%}56.05 f1-score and placed 144. Given our aim and noisy training environment, the results are anticipated.
IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning
Authors: Arik Pamnani, Rajat Goel, Jayesh Choudhari, Mayank Singh
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Chatbot

Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority of these are text-based messaging platforms. Development of Chatbots that automatically understand latent emotions in the textual message is a challenging task. In this paper, we present an automatic emotion detection system that aims to detect the emotion of a person textually conversing with a chatbot. We explore deep learning techniques such as CNN and LSTM based neural networks and outperformed the baseline score by 14{\%}. The trained model and code are kept in public domain.
KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.
Authors: Jasabanta Patro, Nitin Choudhary, Kalpit Chittora, Animesh Mukherjee
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

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.
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation
Authors: Nourah Alswaidan, Mohamed El Bachir Menai
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Recognition

We proposed a model to address emotion recognition in textual conversation based on using automatically extracted features and human engineered features. The proposed model utilizes a fast gated-recurrent-unit backed by CuDNN, and a convolutional neural network to automatically extract features. The human engineered features take the frequency-inverse document frequency of semantic meaning and mood tags extracted from SinticNet.
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification
Authors: Waleed Ragheb, J{\'e}r{\^o}me Az{\'e}, S Bringay, ra, Maximilien Servajean
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Detection and Classification, General Classification, Transfer Learning

This paper addresses the problem of modeling textual conversations and detecting emotions. Our proposed model makes use of 1) deep transfer learning rather than the classical shallow methods of word embedding; 2) self-attention mechanisms to focus on the most important parts of the texts and 3) turn-based conversational modeling for classifying the emotions. The approach does not rely on any hand-crafted features or lexicons. Our model was evaluated on the data provided by the SemEval-2019 shared task on contextual emotion detection in text. The model shows very competitive results.
MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis
Authors: Yoonhyung Lee, Yanghoon Kim, Kyomin Jung
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentiment Analysis

This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue. To address this challenge of predicting the emotion considering its context, we propose a Multi-View Turn-by-Turn (MVTT) model. Firstly, MVTT model generates vectors from each utterance using two encoders: word-level Bi-GRU encoder (WLE) and character-level CNN encoder (CLE). Then, MVTT grasps contextual information by combining the vectors and predict the emotion with the contextual information. We conduct experiments on the effect of vector encoding and vector combination. Our final MVTT model achieved 0.7634 microaveraged F1 score.
MoonGrad at SemEval-2019 Task 3: Ensemble BiRNNs for Contextual Emotion Detection in Dialogues
Authors: Ch Bothe, rakant, Stefan Wermter
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

When reading {``}I don{'}t want to talk to you any more{''}, we might interpret this as either an angry or a sad emotion in the absence of context. Often, the utterances are shorter, and given a short utterance like {``}Me too!{''}, it is difficult to interpret the emotion without context. The lack of prosodic or visual information makes it a challenging problem to detect such emotions only with text. However, using contextual information in the dialogue is gaining importance to provide a context-aware recognition of linguistic features such as emotion, dialogue act, sentiment etc. The SemEval 2019 Task 3 EmoContext competition provides a dataset of three-turn dialogues labeled with the three emotion classes, i.e. Happy, Sad and Angry, and in addition with Others as none of the aforementioned emotion classes. We develop an ensemble of the recurrent neural model with character- and word-level features as an input to solve this problem. The system performs quite well, achieving a microaveraged F1 score (F1Î¼) of 0.7212 for the three emotion classes.
NL-FIIT at SemEval-2019 Task 3: Emotion Detection From Conversational Triplets Using Hierarchical Encoders
Authors: Michal Farkas, Peter Lacko
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

In this paper, we present our system submission for the EmoContext, the third task of the SemEval 2019 workshop. Our solution is a hierarchical recurrent neural network with ELMo embeddings and regularization through dropout and Gaussian noise. We have mainly experimented with two main model architectures: simple and hierarchical LSTM network. We have also examined ensembling of the models and various variants of an ensemble. We have achieved microF1 score of 0.7481, which is significantly higher than baseline and currently the 19th best submission.
NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning
Authors: Rol Potamias, os Alex, ros, Georgios Siolas
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentiment Analysis

Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related data. However, capturing emotional states and the sentiment polarity of written excerpts requires knowledge on the events triggering them. Towards this goal, we present a computational end-to-end context-aware SA methodology, which was competed in the context of the SemEval-2019 / EmoContext task (Task 3). The proposed system is founded on the combination of two neural architectures, a deep recurrent neural network, structured by an attentive Bidirectional LSTM, and a deep dense network (DNN). The system achieved 0.745 micro f1-score, and ranked 26/165 (top 20{\%}) teams among the official task submissions.
PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network
Authors: Luyao Ma, Long Zhang, Wei Ye, Wenhui Hu
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This paper presents the system in SemEval-2019 Task 3, {``}EmoContext: Contextual Emotion Detection in Text{''}. We propose a deep learning architecture with bidirectional LSTM networks, augmented with an emotion-oriented attention network that is capable of extracting emotion information from an utterance. Experimental results show that our model outperforms its variants and the baseline. Overall, this system has achieved 75.57{\%} for the microaveraged F1 score.
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow
Authors: Andrew Nguyen, Tobin South, Nigel Bean, Jonathan Tuke, Lewis Mitchell
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Classification, Emotion Classification, Feature Engineering, General Classification

This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92{\%} of the top score for the competition, demonstrating that {``}shallow{''} classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis.
SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning
Authors: Zinedine Rebiai, Simon Andersen, Antoine Debrenne, Victor Lafargue
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentiment Analysis

In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset.
Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations
Authors: Jacob Anderson
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Clustering

In this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper{'}s contributions include a method for handling any sized input and a method for breaking down the conversation into separate parts for easier processing. Finally, clustering was shown to improve results and that such a model for handling sentiment in conversations is both fast and accurate.
SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations
Authors: Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonz{\'a}lez, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Classification, General Classification

Detecting emotions in textual conversation is a challenging problem in absence of nonverbal cues typically associated with emotion, like fa- cial expression or voice modulations. How- ever, more and more users are using message platforms such as WhatsApp or Telegram. For this reason, it is important to develop systems capable of understanding human emotions in textual conversations. In this paper, we carried out different systems to analyze the emotions of textual dialogue from SemEval-2019 Task 3: EmoContext for English language. Our main contribution is the integration of emotional and sentimental features in the classification using the SVM algorithm.
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
Authors: Sanghwan Bae, Jihun Choi, Sang-goo Lee
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Recognition in Conversation, General Classification

We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consistently show positive effects on the performance. Also we propose a novel neural architecture which utilizes representation of overall context as well as of each utterance. The combination of the methods and the models achieved micro F1 score of about 0.766 on the final evaluation.
SSN\_NLP at SemEval-2019 Task 3: Contextual Emotion Identification from Textual Conversation using Seq2Seq Deep Neural Network
Authors: Senthil Kumar B., Thenmozhi D., Ch, Aravindan rabose, Srinethe Sharavanan
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

Emotion identification is a process of identifying the emotions automatically from text, speech or images. Emotion identification from textual conversations is a challenging problem due to absence of gestures, vocal intonation and facial expressions. It enables conversational agents, chat bots and messengers to detect and report the emotions to the user instantly for a healthy conversation by avoiding emotional cues and miscommunications. We have adopted a Seq2Seq deep neural network to identify the emotions present in the text sequences. Several layers namely embedding layer, encoding-decoding layer, softmax layer and a loss layer are used to map the sequences from textual conversations to the emotions namely Angry, Happy, Sad and Others. We have evaluated our approach on the EmoContext@SemEval2019 dataset and we have obtained the micro-averaged F1 scores as 0.595 and 0.6568 for the pre-evaluation dataset and final evaluation test set respectively. Our approach improved the base line score by 7{\%} for final evaluation test set.
SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networks
Authors: Marco Polignano, Marco de Gemmis, Giovanni Semeraro
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence, Word Embeddings

Emotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features. Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence. Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated. Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task. The final system configuration achieved a micro F1 score of 0.7089. The python code of the system is fully available at https://github.com/marcopoli/EmoContext2019
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
Authors: Angelo Basile, Marc Franco-Salvador, Neha Pawar, Sanja {\v{S}}tajner, Mara Chinea Rios, Yassine Benajiba
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Chatbot, Emotion Classification, Emotion Recognition in Conversation, General Classification

In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.
TDBot at SemEval-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification Approach
Authors: Sourabh Maity
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification

With the system description it is shown how to use the context information while detecting the emotion in a dialogue. Some guidelines about how to handle emojis was also laid out. While developing this system I realized the importance of pre-processing in conversational text data, or in general NLP related tasks; it can not be over emphasized.
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN
Authors: Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Classification, Emotion Recognition, General Classification, Word Embeddings

With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users. It is important to understand the emotions through conversations to improve the quality of dialog systems. To facilitate the researches on dialog emotion recognition, the SemEval-2019 Task 3 named EmoContext is proposed. This task aims to classify the emotions of user utterance along with two short turns of dialogues into four categories. In this paper, we propose an attentional LSTM-CNN model to participate in this shared task. We use a combination of convolutional neural networks and long-short term neural networks to capture both local and long-distance contextual information in conversations. In addition, we apply attention mechanism to recognize and attend to important words within conversations. Besides, we propose to use ensemble strategies by combing the variants of our model with different pre-trained word embeddings via weighted voting. Our model achieved 0.7542 micro-F1 score in the final test data, ranking 15{\^{}}th out of 165 teams.
THU-HCSI at SemEval-2019 Task 3: Hierarchical Ensemble Classification of Contextual Emotion in Conversation
Authors: Xihao Liang, Ye Ma, Mingxing Xu
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification

In this paper, we describe our hierarchical ensemble system designed for the SemEval-2019 task3, EmoContext. In our system, three sets of classifiers are trained for different sub-targets and the predicted labels of these base classifiers are combined through three steps of voting to make the final prediction. Effective details for developing base classifiers are highlighted.
TokyoTech\_NLP at SemEval-2019 Task 3: Emotion-related Symbols in Emotion Detection
Authors: Zhishen Yang, Sam Vijlbrief, Naoaki Okazaki
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This paper presents our contextual emotion detection system in approaching the SemEval2019 shared task 3: EmoContext: Contextual Emotion Detection in Text. This system cooperates with an emotion detection neural network method (Poria et al., 2017), emoji2vec (Eisner et al., 2016) embedding, word2vec embedding (Mikolov et al., 2013), and our proposed emoticon and emoji preprocessing method. The experimental results demonstrate the usefulness of our emoticon and emoji prepossessing method, and representations of emoticons and emoji contribute model{'}s emotion detection.
UAIC at SemEval-2019 Task 3: Extracting Much from Little
Authors: Cristian Simionescu, Ingrid Stoleru, Diana Lucaci, Gheorghe Balan, Iulian Bute, Adrian Iftene
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentiment Analysis

In this paper, we present a system description for implementing a sentiment analysis agent capable of interpreting the state of an interlocutor engaged in short three message conversations. We present the results and observations of our work and which parts could be further improved in the future.
YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation
Authors: Dawei Li, Jin Wang, Xue-jie Zhang
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Ensemble Learning, Sentiment Analysis

This paper describes our approach to the sentiment analysis of Twitter textual conversations based on deep learning. We analyze the syntax, abbreviations, and informal-writing of Twitter; and perform perfect data preprocessing on the data to convert them to normative text. We apply a multi-step ensemble strategy to solve the problem of extremely unbalanced data in the training set. This is achieved by taking the GloVe and Elmo word vectors as input into a combination model with four different deep neural networks. The experimental results from the development dataset demonstrate that the proposed model exhibits a strong generalization ability. For evaluation on the best dataset, we integrated the results using the stacking ensemble learning approach and achieved competitive results. According to the final official review, the results of our model ranked 10th out of 165 teams.