ymoslem/acl-abstracts huggingface dataset:
UTFPR at IEST 2018: Exploring Character-to-Word Composition for Emotion Analysis (2018)

We introduce the UTFPR system for the Implicit Emotions Shared Task of 2018: A compositional character-to-word recurrent neural network that does not exploit heavy and/or hard-to-obtain resources. We find that our approach can outperform multiple baselines, and offers an elegant and effective solution to the problem of orthographic variance in tweets.
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition (2018)

This paper describes the approaches used in HUMIR system for the WASSA-2018 shared task on the implicit emotion recognition. The objective of this task is to predict the emotion expressed by the target word that has been excluded from the given tweet. We suppose this task as a word sense disambiguation in which the target word is considered as a synthetic word that can express 6 emotions depending on the context. To predict the correct emotion, we propose a deep neural network model that uses two BiLSTM networks to represent the contexts in the left and right sides of the target word. The BiLSTM outputs achieved from the left and right contexts are considered as context-sensitive features. These features are used in a feed-forward neural network to predict the target word emotion. Besides this approach, we also combine the BiLSTM model with lexicon-based and emotion-based features. Finally, we employ all models in the final system using Bagging ensemble method. We achieved macro F-measure value of 68.8 on the official test set and ranked sixth out of 30 participants.
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification (2018)

This paper describes our method that competed at WASSA2018 \textit{Implicit Emotion Shared Task}. The goal of this task is to classify the emotions of excluded words in tweets into six different classes: sad, joy, disgust, surprise, anger and fear. For this, we examine a BiLSTM architecture with attention mechanism (BiLSTM-Attention) and a LSTM architecture with attention mechanism (LSTM-Attention), and try different dropout rates based on these two models. We then exploit an ensemble of these methods to give the final prediction which improves the model performance significantly compared with the baseline model. The proposed method achieves 7th position out of 30 teams and outperforms the baseline method by 12.5{\%} in terms of \textit{macro F1}.
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification (2018)

In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds.
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets (2018)

This paper describes our system submitted to IEST 2018, a shared task (Klinger et al., 2018) to predict the emotion types. Six emotion types are involved: anger, joy, fear, surprise, disgust and sad. We perform three different approaches: feed forward neural network (FFNN), convolutional BLSTM (ConBLSTM) and Gradient Boosting Regression Tree Method (GBM). Word embeddings used in convolutional BLSTM are pre-trained on 470 million tweets which are filtered using the emotional words and emojis. In addition, broad sets of features (i.e. syntactic features, lexicon features, cluster features) are adopted to train GBM and FFNN. The three approaches are finally ensembled by the weighted average of predicted probabilities of each emotion label.
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets (2018)

This paper describes our system designed for the WASSA-2018 Implicit Emotion Shared Task (IEST). The task is to predict the emotion category expressed in a tweet by removing the terms \textit{angry}, \textit{afraid}, \textit{happy}, \textit{sad}, \textit{surprised}, \textit{disgusted} and their synonyms. Our final submission is an ensemble of one supervised learning model and three deep neural network based models, where each model approaches the problem from essentially different directions. Our system achieves the macro F1 score of 65.8{\%}, which is a 5.9{\%} performance improvement over the baseline and is ranked 12 out of 30 participating teams.
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning (2018)

This paper describes an approach to solve implicit emotion classification with the use of pre-trained word embedding models to train multiple neural networks. The system described in this paper is composed of a sequential combination of Long Short-Term Memory and Convolutional Neural Network for feature extraction and Feedforward Neural Network for classification. In this paper, we successfully show that features extracted using multiple pre-trained embeddings can be used to improve the overall performance of the system with Emoji being one of the significant features. The evaluations show that our approach outperforms the baseline system by more than 8{\%} without using any external corpus or lexicon. This approach is ranked 8th in Implicit Emotion Shared Task (IEST) at WASSA-2018.
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing (2018)

In this paper, we present neural models submitted to Shared Task on Implicit Emotion Recognition, organized as part of WASSA 2018. We propose a Bi-LSTM architecture with regularization through dropout and Gaussian noise. Our models use three different embedding layers: GloVe word embeddings trained on Twitter dataset, ELMo embeddings and also sentence embeddings. We see preprocessing as one of the most important parts of the task. We focused on handling emojis, emoticons, hashtags, and also various shortened word forms. In some cases, we proposed to remove some parts of the text, as they do not affect emotion of the original sentence. We also experimented with other modifications like category weights for learning and stacking multiple layers. Our model achieved a macro average F1 score of 65.55{\%}, significantly outperforming the baseline model produced by a simple logistic regression.
UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network (2018)

This paper describes our system created for the WASSA 2018 Implicit Emotion Shared Task. The goal of this task is to predict the emotion of a given tweet, from which a certain emotion word is removed. The removed word can be \textit{sad}, \textit{happy}, \textit{disgusted}, \textit{angry}, \textit{afraid} or a synonym of one of them. Our proposed system is based on deep-learning methods. We use Bidirectional Long Short-Term Memory (BiLSTM) with word embeddings as an input. Pre-trained DeepMoji model and pre-trained emoji2vec emoji embeddings are also used as additional inputs. Our System achieves 0.657 macro F1 score and our rank is 13th out of 30.
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection (2018)

This paper describes the participation of USI-IR in WASSA 2018 Implicit Emotion Shared Task. We propose a relevance feedback approach employing a sequential model (biLSTM) and word embeddings derived from a large collection of tweets. To this end, we assume that the top-\textit{k} predictions produce at a first classification step are correct (based on the model accuracy) and use them as new examples to re-train the network.
EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions (2018)

EmotiKLUE is a submission to the Implicit Emotion Shared Task. It is a deep learning system that combines independent representations of the left and right contexts of the emotion word with the topic distribution of an LDA topic model. EmotiKLUE achieves a macro average \textit{F₁}score of 67.13{\%}, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved \textit{F₁}score of 68.10{\%}.
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification (2018)

We present \textit{BrainT}, a multi-class, averaged perceptron tested on implicit emotion prediction of tweets. We show that the dataset is linearly separable and explore ways in fine-tuning the baseline classifier. Our results indicate that the bag-of-words features benefit the model moderately and prediction can be improved with bigrams, trigrams, \textit{skip-one}-tetragrams and POS-tags. Furthermore, we find preprocessing of the n-grams, including stemming, lowercasing, stopword filtering, emoji and emoticon conversion generally not useful. The model is trained on an annotated corpus of 153,383 tweets and predictions on the test data were submitted to the WASSA-2018 Implicit Emotion Shared Task. BrainT attained a Macro F-score of 0.63.
Disney at IEST 2018: Predicting Emotions using an Ensemble (2018)

This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focuses on implicit emotion prediction in a tweet. In this task, keywords corresponding to the six emotion labels used (anger, fear, disgust, joy, sad, and surprise) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on an ensemble of classifiers for prediction. Each classifier uses a sequence of Convolutional Neural Network (CNN) architecture blocks and uses ELMo (Embeddings from Language Model) as an input. Our system achieves a 66.2{\%} F1 score on the test set. The best performing system in the shared task has reported a 71.4{\%} F1 score.
Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection (2018)

In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task. The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed. The idea is to come up with a model which has the ability to implicitly identify the emotion expressed given the context words. We have used a Gated Recurrent Neural Network (GRU) and a Capsule Network based model for the task. Pre-trained word embeddings have been utilized to incorporate contextual knowledge about words into the model. GRU layer learns latent representations using the input word embeddings. Subsequent Capsule Network layer learns high-level features from that hidden representation. The proposed model managed to achieve a macro-F1 score of 0.692.
Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning (2018)

This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6{\%} compared to the source model`s accuracy in the legal domain.
What Makes You Stressed? Finding Reasons From Tweets (2018)

Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals. This paper introduces a novel framework for finding reasons for stress from tweets, analyzing multiple categories for the first time. Three word-vector based methods are evaluated on collections of tweets about politics or airlines and are found to be more accurate than standard machine learning algorithms.
EmojiGAN: learning emojis distributions with a generative model (2018)

Generative models have recently experienced a surge in popularity due to the development of more efficient training algorithms and increasing computational power. Models such as adversarial generative networks (GANs) have been successfully used in various areas such as computer vision, medical imaging, style transfer and natural language generation. Adversarial nets were recently shown to yield results in the image-to-text task, where given a set of images, one has to provide their corresponding text description. In this paper, we take a similar approach and propose a image-to-emoji architecture, which is trained on data from social networks and can be used to score a given picture using ideograms. We show empirical results of our algorithm on data obtained from the most influential Instagram accounts.
Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions (2018)

Analysis of the topics mentioned and opinions expressed in parliamentary debate motions{--}or proposals{--}is difficult for human readers, but necessary for understanding and automatic processing of the content of the subsequent speeches. We present a dataset of debate motions with pre-existing {\textquoteleft}\textit{policy}' labels, and investigate the utility of these labels for simultaneous topic and opinion polarity analysis. For topic detection, we apply one-versus-the-rest supervised topic classification, finding that good performance is achieved in predicting the \textit{policy} topics, and that textual features derived from the debate titles associated with the motions are particularly indicative of motion topic. We then examine whether the output could also be used to determine the positions taken by proposers towards the different policies by investigating how well humans agree in interpreting the opinion polarities of the motions. Finding very high levels of agreement, we conclude that the \textit{policies} used can be reliable labels for use in these tasks, and that successful topic detection can therefore provide opinion analysis of the motions {\textquoteleft}for free'.
Homonym Detection For Humor Recognition In Short Text (2018)

In this paper, automatic homophone- and homograph detection are suggested as new useful features for humor recognition systems. The system combines style-features from previous studies on humor recognition in short text with ambiguity-based features. The performance of two potentially useful homograph detection methods is evaluated using crowdsourced annotations as ground truth. Adding homophones and homographs as features to the classifier results in a small but significant improvement over the style-features alone. For the task of humor recognition, recall appears to be a more important quality measure than precision. Although the system was designed for humor recognition in oneliners, it also performs well at the classification of longer humorous texts.
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training (2018)

In this paper, we propose Emo2Vec which encodes emotional semantics into vectors. We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection, and personality recognition. Our evaluation of Emo2Vec shows that it outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora. When concatenated with GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on several tasks using a simple logistic regression classifier.
Learning representations for sentiment classification using Multi-task framework (2018)

Most of the existing state of the art sentiment classification techniques involve the use of pre-trained embeddings. This paper postulates a generalized representation that collates training on multiple datasets using a Multi-task learning framework. We incorporate publicly available, pre-trained embeddings with Bidirectional LSTM`s to develop the multi-task model. We validate the representations on an independent test Irony dataset that can contain several sentiments within each sample, with an arbitrary distribution. Our experiments show a significant improvement in results as compared to the available baselines for individual datasets on which independent models are trained. Results also suggest superior performance of the representations generated over Irony dataset.
Super Characters: A Conversion from Sentiment Classification to Image Classification (2018)

We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text features are extracted automatically from the generated Super Characters images, hence there is no need of any explicit step of embedding the words or characters into numerical vector representations. Experimental results on large social media corpus show that the Super Characters method consistently outperforms other methods for sentiment classification and topic classification tasks on ten large social media datasets of millions of contents in four different languages, including Chinese, Japanese, Korean and English.
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs (2018)

Comments on web news contain controversies that manifest as inter-group agreement-conflicts. Tracking such \textit{rapidly evolving controversy} could ease conflict resolution or journalist-user interaction. However, this presupposes controversy online-prediction that scales to diverse domains using incidental supervision signals instead of manual labeling. To more deeply interpret comment-controversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder. Finally, we use ablation and interpretability methods to determine the impacts of topic, discourse and sentiment indicators, contextual vs. global word influence, as well as genre-keywords vs. per-genre-controversy keywords {--} to find that the models learn plausible controversy features using only incidentally supervised signals.
Words Worth: Verbal Content and Hirability Impressions in YouTube Video Resumes (2018)

Automatic hirability prediction from video resumes is gaining increasing attention in both psychology and computing. Most existing works have investigated hirability from the perspective of nonverbal behavior, with verbal content receiving little interest. In this study, we leverage the advances in deep-learning based text representation techniques (like word embedding) in natural language processing to investigate the relationship between verbal content and perceived hirability ratings. To this end, we use 292 conversational video resumes from YouTube, develop a computational framework to automatically extract various representations of verbal content, and evaluate them in a regression task. We obtain a best performance of R{\texttwosuperior} = 0.23 using GloVe, and R{\texttwosuperior} = 0.22 using Word2Vec representations for manual and automatically transcribed texts respectively. Our inference results indicate the feasibility of using deep learning based verbal content representation in inferring hirability scores from online conversational video resumes.
Predicting Adolescents' Educational Track from Chat Messages on Dutch Social Media (2018)

We aim to predict Flemish adolescents' educational track based on their Dutch social media writing. We distinguish between the three main types of Belgian secondary education: General (theory-oriented), Vocational (practice-oriented), and Technical Secondary Education (hybrid). The best results are obtained with a Naive Bayes model, i.e. an F-score of 0.68 (std. dev. 0.05) in 10-fold cross-validation experiments on the training data and an F-score of 0.60 on unseen data. Many of the most informative features are character n-grams containing specific occurrences of chatspeak phenomena such as emoticons. While the detection of the most theory- and practice-oriented educational tracks seems to be a relatively easy task, the hybrid Technical level appears to be much harder to capture based on online writing style, as expected.
Arabizi sentiment analysis based on transliteration and automatic corpus annotation (2018)

Arabizi is a form of writing Arabic text which relies on Latin letters, numerals and punctuation rather than Arabic letters. In the literature, the difficulties associated with Arabizi sentiment analysis have been underestimated, principally due to the complexity of Arabizi. In this paper, we present an approach to automatically classify sentiments of Arabizi messages into positives or negatives. In the proposed approach, Arabizi messages are first transliterated into Arabic. Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus. For corpus validation, shallow machine learning algorithms such as Support Vectors Machine (SVM) and Naive Bays (NB) are used. Simulations results demonstrate the outperformance of NB algorithm over all others. The highest achieved F1-score is up to 78{\%} and 76{\%} for manually and automatically transliterated dataset respectively. Ongoing work is aimed at improving the transliterator module and annotated sentiment dataset.
UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models (2018)

We describe UBC-NLP contribution to IEST-2018, focused at learning implicit emotion in Twitter data. Among the 30 participating teams, our system ranked the 4th (with 69.3{\%} \textit{F}-score). Post competition, we were able to score slightly higher than the 3rd ranking system (reaching 70.7{\%}). Our system is trained on top of a pre-trained language model (LM), fine-tuned on the data provided by the task organizers. Our best results are acquired by an average of an ensemble of language models. We also offer an analysis of system performance and the impact of training data size on the task. For example, we show that training our best model for only one epoch with {\ensuremath{<}} 40{\%} of the data enables better performance than the baseline reported by Klinger et al. (2018) for the task.
Scaling Neural Machine Translation (2018)

Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8-GPU machine with careful tuning and implementation. On WMT`14 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on the much larger Paracrawl dataset. On the WMT`14 English-French task, we obtain a state-of-the-art BLEU of 43.2 in 8.5 hours on 128 GPUs.