pwc-archive/papers-with-abstracts huggingface dataset:
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing
Authors: Samuel Pecar, Michal Farkas, Marian Simko, Peter Lacko, Maria Bielikova
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Recognition, Sentence, Sentence Embeddings, Sentiment Analysis, Word Embeddings

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
Authors: Pavel P{\v{r}}ib{\'a}{\v{n}}, Ji{\v{r}}{\'\i} Mart{\'\i}nek
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sentiment Analysis, Word Embeddings

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
Authors: Esteban R{\'\i}ssola, Anastasia Giachanou, Fabio Crestani
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Recognition, General Classification, Sentiment Analysis, Word Embeddings

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
Authors: Thomas Proisl, Philipp Heinrich, Besim Kabashi, Stefan Evert
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, Opinion Mining

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
Authors: Vachagan Gratian, Marina Haid
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, Emotion Recognition, General Classification, POS, Sentiment Analysis

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
Authors: Wojciech Witon, Pierre Colombo, Ashutosh Modi, Mubbasir Kapadia
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Emotion Classification, Language Modeling, Language Modelling, Prediction, Sentence Classification, Sentence Embedding, Sentiment Analysis

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.
What Makes You Stressed? Finding Reasons From Tweets
Authors: Reshmi Gopalakrishna Pillai, Mike Thelwall, Constantin Orasan
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: BIG-bench Machine Learning

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
Authors: Bogdan Mazoure, Thang Doan, Saibal Ray
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Image Captioning, Image to text, Style Transfer, Text Generation

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
Authors: Gavin Abercrombie, Riza Theresa Batista-Navarro
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sentiment Analysis, Topic Classification

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 {`}\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 {`}for free{'}.
Homonym Detection For Humor Recognition In Short Text
Authors: Sven van den Beukel, Lora Aroyo
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification

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.
Learning representations for sentiment classification using Multi-task framework
Authors: Hardik Meisheri, Harshad Khadilkar
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, Multi-Task Learning, Sentiment Analysis, Sentiment Classification

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.
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs
Authors: Nils Rethmeier, Marc H{\"u}bner, Leonhard Hennig
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Binary Classification, Language Modeling, Language Modelling

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
Authors: Sk Muralidhar, a, Laurent Nguyen, Daniel Gatica-Perez
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Automatic Speech Recognition (ASR), Speech Recognition

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{\mbox{$^2$}} = 0.23 using GloVe, and R{\mbox{$^2$}} = 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
Authors: Lisa Hilte, Walter Daelemans, V, Reinhild ekerckhove
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

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
Authors: Imane Guellil, Ahsan Adeel, Faical Azouaou, Fodil Benali, Ala-eddine Hachani, Amir Hussain
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Opinion Mining, Sentiment Analysis, Transliteration

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
Authors: Hassan Alhuzali, Mohamed Elaraby, Muhammad Abdul-Mageed
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Language Modeling, Language Modelling

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 {\textless} 40{\%} of the data enables better performance than the baseline reported by Klinger et al. (2018) for the task.
Neural Machine Translation of Logographic Language Using Sub-character Level Information
Authors: Longtu Zhang, Mamoru Komachi
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Decoder, Machine Translation, NMT, Translation, Word Embeddings

Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units. However, important differences between languages with logographic and alphabetic writing systems have long been overlooked. This study focuses on these differences and uses a simple approach to improve the performance of NMT systems utilizing decomposed sub-character level information for logographic languages. Our results indicate that our approach not only improves the translation capabilities of NMT systems between Chinese and English, but also further improves NMT systems between Chinese and Japanese, because it utilizes the shared information brought by similar sub-character units.
Discourse-Related Language Contrasts in English-Croatian Human and Machine Translation
Authors: Margita {\v{S}}o{\v{s}}tari{\'c}, Christian Hardmeier, Sara Stymne
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, NMT, Translation, Word Alignment

We present an analysis of a number of coreference phenomena in English-Croatian human and machine translations. The aim is to shed light on the differences in the way these structurally different languages make use of discourse information and provide insights for discourse-aware machine translation system development. The phenomena are automatically identified in parallel data using annotation produced by parsers and word alignment tools, enabling us to pinpoint patterns of interest in both languages. We make the analysis more fine-grained by including three corpora pertaining to three different registers. In a second step, we create a test set with the challenging linguistic constructions and use it to evaluate the performance of three MT systems. We show that both SMT and NMT systems struggle with handling these discourse phenomena, even though NMT tends to perform somewhat better than SMT. By providing an overview of patterns frequently occurring in actual language use, as well as by pointing out the weaknesses of current MT systems that commonly mistranslate them, we hope to contribute to the effort of resolving the issue of discourse phenomena in MT applications.
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments
Authors: Dario Stojanovski, Alex Fraser, er
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Coreference Resolution, Language Modeling, Language Modelling, Machine Translation, NMT, Sentence, Translation

Cross-sentence context can provide valuable information in Machine Translation and is critical for translation of anaphoric pronouns and for providing consistent translations. In this paper, we devise simple oracle experiments targeting coreference and coherence. Oracles are an easy way to evaluate the effect of different discourse-level phenomena in NMT using BLEU and eliminate the necessity to manually define challenge sets for this purpose. We propose two context-aware NMT models and compare them against models working on a concatenation of consecutive sentences. Concatenation models perform better, but are computationally expensive. We show that NMT models taking advantage of context oracle signals can achieve considerable gains in BLEU, of up to 7.02 BLEU for coreference and 1.89 BLEU for coherence on subtitles translation. Access to strong signals allows us to make clear comparisons between context-aware models.
Improving Neural Language Models with Weight Norm Initialization and Regularization
Authors: Christian Herold, Yingbo Gao, Hermann Ney
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Machine Translation, Speech Recognition

Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies. We examine such matrices in fine-tuned language models and observe that a NLM learns word vectors whose norms are related to the word frequencies. We show that by initializing the weight norms with scaled log word counts, together with other techniques, lower perplexities can be obtained in early epochs of training. We also introduce a weight norm regularization loss term, whose hyperparameters are tuned via a grid search. With this method, we are able to significantly improve perplexities on two word-level language modeling tasks (without dynamic evaluation): from 54.44 to 53.16 on Penn Treebank (PTB) and from 61.45 to 60.13 on WikiText-2 (WT2).
Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods
Authors: Catarina Cruz Silva, Chao-Hong Liu, Alberto Poncelas, Andy Way
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, NMT, Translation

Data selection is a process used in selecting a subset of parallel data for the training of machine translation (MT) systems, so that 1) resources for training might be reduced, 2) trained models could perform better than those trained with the whole corpus, and/or 3) trained models are more tailored to specific domains. It has been shown that for statistical MT (SMT), the use of data selection helps improve the MT performance significantly. In this study, we reviewed three data selection approaches for MT, namely Term Frequency{--} Inverse Document Frequency, Cross-Entropy Difference and Feature Decay Algorithm, and conducted experiments on Neural Machine Translation (NMT) with the selected data using the three approaches. The results showed that for NMT systems, using data selection also improved the performance, though the gain is not as much as for SMT systems.
Input Combination Strategies for Multi-Source Transformer Decoder
Authors: Jind{\v{r}}ich Libovick{\'y}, Jind{\v{r}}ich Helcl, David Mare{\v{c}}ek
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Decoder, Image Captioning, Machine Translation, Multimodal Machine Translation, Text Summarization, Translation

In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways. This topic has been thoroughly studied on recurrent architectures. In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture. We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical. We evaluate our methods on tasks of multimodal translation and translation with multiple source languages. The experiments show that the models are able to use multiple sources and improve over single source baselines.
Findings of the 2018 Conference on Machine Translation (WMT18)
Authors: Ond{\v{r}}ej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, Christof Monz
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Automatic Post-Editing, Machine Translation, Multimodal Machine Translation, Translation

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation.