pwc-archive/papers-with-abstracts huggingface dataset:
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification
Authors: Juan Manuel P{\'e}rez, Franco M. Luque
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, Sentiment Analysis, Word Embeddings

In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag-of-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations. In particular, we trained robust task-oriented subword-aware embeddings and computed tweet representations using a weighted-averaging strategy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively.
FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter
Authors: Vijayasaradhi Indurthi, Bakhtiyar Syed, Manish Shrivastava, Nikhil Chakravartula, Manish Gupta, Vasudeva Varma
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Position, Sentence, Sentence Embeddings

This paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. We participated in the subtask A for English and ranked first in the evaluation on the test set. We evaluate the quality of multiple sentence embeddings and explore multiple training models to evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team - Fermi{'}s model achieved an accuracy of 65.00{\%} for English language in task A. Our models, which use pretrained Universal Encoder sentence embeddings for transforming the input and SVM (with RBF kernel) for classification, scored first position (among 68) in the leaderboard on the test set for Subtask A in English language. In this paper we provide a detailed description of the approach, as well as the results obtained in the task.
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers
Authors: Ping Liu, Wen Li, Liang Zou
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, Language Identification, text-classification, Text Classification, Transfer Learning

Transfer learning and domain adaptive learning have been applied to various fields including computer vision (e.g., image recognition) and natural language processing (e.g., text classification). One of the benefits of transfer learning is to learn effectively and efficiently from limited labeled data with a pre-trained model. In the shared task of identifying and categorizing offensive language in social media, we preprocess the dataset according to the language behaviors on social media, and then adapt and fine-tune the Bidirectional Encoder Representation from Transformer (BERT) pre-trained by Google AI Language team. Our team NULI wins the first place (1st) in Sub-task A - Offensive Language Identification and is ranked 4th and 18th in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offense Target Identification respectively.
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCA
Authors: Weimin Lyu, Sheng Huang, Abdul Rafae Khan, Shengqiang Zhang, Weiwei Sun, Jia Xu
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Semantic Parsing

This paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA. We introduce a novel model by applying a cascaded MLP and BiLSTM model. Then, we ensemble multiple system-outputs by reparsing. In particular, we introduce a new decoding algorithm for building the UCCA representation. Our system won the first place in one track (French-20K-Open), second places in four tracks (English-Wiki-Open, English-20K-Open, German-20K-Open, and German-20K-Closed), and third place in one track (English-20K-Closed), among all seven tracks.
DANGNT@UIT.VNU-HCM at SemEval 2019 Task 1: Graph Transformation System from Stanford Basic Dependencies to Universal Conceptual Cognitive Annotation (UCCA)
Authors: Dang Tuan Nguyen, Trung Tran
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Form, Semantic Parsing

This paper describes the graph transfor-mation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA)1. The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form. The output of GT System is the corresponding full UCCA xml. Based on the idea of graph illustration and transformation, we perform four main tasks when building GT System. At the first task, we illustrate the graph form of stanford dependencies2 of input text. We then transform into an intermediate graph in the second task. At the third task, we continue to transform into ouput graph form. Finally, we create the output UCCA xml. The evaluation results show that our method generates good-quality UCCA xml and has a meaningful contribution to the semantic represetation sub-field in Natural Language Processing.
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
Authors: Shiva Taslimipoor, Omid Rohanian, Sara Mo{\v{z}}e
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Dependency Parsing, Semantic Dependency Parsing, Semantic Parsing

This paper describes the system submitted to the SemEval 2019 shared task 1 {`}Cross-lingual Semantic Parsing with UCCA{'}. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing.
T\"upa at SemEval-2019 Task1: (Almost) feature-free Semantic Parsing
Authors: Tobias P{\"u}tz, Kevin Glocker
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: All, Data Augmentation, Semantic Parsing, Word Embeddings

Our submission for Task 1 {`}Cross-lingual Semantic Parsing with UCCA{'} at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph as an aggregation of their terminal children. We further demonstrate how augmenting data using the baseline systems provides a consistent advantage in all open submission tracks. We submitted results to all open tracks (English, in- and out-of-domain, German in-domain and French in-domain, low-resource). Our system achieves competitive performance in all settings besides the French, where we did not augment the data. Post-evaluation experiments showed that data augmentation is especially crucial in this setting.
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder
Authors: Dian Yu, Kenji Sagae
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Decoder, Feature Engineering, Semantic Parsing

We present an encoder-decoder model for semantic parsing with UCCA SemEval 2019 Task 1. The encoder is a Bi-LSTM and the decoder uses recursive self-attention. The proposed model alleviates challenges and feature engineering in traditional transition-based and graph-based parsers. The resulting parser is simple and proved to effective on the semantic parsing task.
L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations
Authors: Eug{\'e}nio Ribeiro, V{\^a}nia Mendon{\c{c}}a, Ricardo Ribeiro, David Martins de Matos, Alberto Sardinha, Ana L{\'u}cia Santos, Lu{\'\i}sa Coheur
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Clustering, Graph Clustering, Task 2, Word Sense Disambiguation

Building large datasets annotated with semantic information, such as FrameNet, is an expensive process. Consequently, such resources are unavailable for many languages and specific domains. This problem can be alleviated by using unsupervised approaches to induce the frames evoked by a collection of documents. That is the objective of the second task of SemEval 2019, which comprises three subtasks: clustering of verbs that evoke the same frame and clustering of arguments into both frame-specific slots and semantic roles. We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments. Using such representations is appropriate in the context of this task, since they provide cues for word-sense disambiguation. Thus, they can be used to identify different frames evoked by the same words. Using this approach we were able to outperform all of the baselines reported for the task on the test set in terms of Purity F1, as well as in terms of BCubed F1 in most cases.
BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction
Authors: Vachagan Gratian
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Classification, General Classification

The paper describes an ensemble of linear perceptrons trained for emotion classification as part of the SemEval-2019 shared-task 3. The model uses a matrix of probabilities to weight the activations of the base-classifiers and makes a final prediction using the sum rule. The base-classifiers are multi-class perceptrons utilizing character and word n-grams, part-of-speech tags and sentiment polarity scores. The results of our experiments indicate that the ensemble outperforms the base-classifiers, but only marginally. In the best scenario our model attains an F-Micro score of 0.672, whereas the base-classifiers attained scores ranging from 0.636 to 0.666.
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Authors: Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Jamin Shin, Yan Xu, Peng Xu, Pascale Fung
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Classification, Gaussian Processes, General Classification

Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions. We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-to-end neural network models, and Gaussian processes for automatic hyper-parameter search. In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77{\%} F1-score on the test set.
CECL at SemEval-2019 Task 3: Using Surface Learning for Detecting Emotion in Textual Conversations
Authors: Yves Bestgen
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This paper describes the system developed by the Centre for English Corpus Linguistics for the SemEval-2019 Task 3: EmoContext. It aimed at classifying the emotion of a user utterance in a textual conversation as happy, sad, angry or other. It is based on a large number of feature types, mainly unigrams and bigrams, which were extracted by a SAS program. The usefulness of the different feature types was evaluated by means of Monte-Carlo resampling tests. As this system does not rest on any deep learning component, which is currently considered as the state-of-the-art approach, it can be seen as a possible point of comparison for such kind of systems.
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM
Authors: Elham Mohammadi, Hessam Amini, Leila Kosseim
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: POS, Word Embeddings

This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification.This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12{\%}.
CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation
Authors: Joseph Cummings, Jason Wilson
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

With text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation. We use a Naive Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance. Additionally, we find that using context while train-ing the model significantly decreases performance. Our approach has the additional bene-fit that its performance rivals a baseline LSTM model while requiring fewer resources.
CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection
Authors: Changjie Li, Yun Xing
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Language Modeling, Language Modelling

In this paper, we describe the participation of team {''}CLP{''} in SemEval-2019 Task 3 {``}Con- textual Emotion Detection in Text{''} that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656.
CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs
Authors: Ana Valeria Gonz{\'a}lez, Victor Petr{\'e}n Bach Hansen, Joachim Bingel, Anders S{\o}gaard
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification

This work describes the system presented by the CoAStaL Natural Language Processing group at University of Copenhagen. The main system we present uses the same attention mechanism presented in (Yang et al., 2016). Our overall model architecture is also inspired by their hierarchical classification model and adapted to deal with classification in dialogue by encoding information at the turn level. We use different encodings for each turn to create a more expressive representation of dialogue context which is then fed into our classifier.We also define a custom preprocessing step in order to deal with language commonly used in interactions across many social media outlets. Our proposed system achieves a micro F1 score of 0.7340 on the test set and shows significant gains in performance compared to a system using dialogue level encoding.
ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector
Authors: Rafa{\l} Po{\'s}wiata
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Emotion Recognition in Conversation, General Classification

This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of context, identify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Others. Our system: ConSSED is a configurable combination of semantic and sentiment neural models. The official task submission achieved a micro-average F1 score of 75.31 which placed us 16th out of 165 participating systems.
CX-ST-RNM at SemEval-2019 Task 3: Fusion of Recurrent Neural Networks Based on Contextualized and Static Word Representations for Contextual Emotion Detection
Authors: Micha{\l} Pere{\l}kiewicz
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

In this paper, I describe a fusion model combining contextualized and static word representations for approaching the EmoContext task in the SemEval 2019 competition. The model is based on two Recurrent Neural Networks, the first one is fed with a state-of-the-art ELMo deep contextualized word representation and the second one is fed with a static Word2Vec embedding augmented with 10-dimensional affective word feature vector. The proposed model is compared with two baseline models based on a static word representation and a contextualized word representation, separately. My approach achieved officially 0.7278 microaveraged F1 score on the test dataset, ranking 47th out of 165 participants.
ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis
Authors: Akansha Jain, Ishita Aggarwal, Ankit Singh
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Domain Adaptation, Emotion Recognition, Sentence

This paper describes our proposed system {\&} experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3. We exploit sentiment information, syntactic patterns {\&} semantic relatedness to capture diverse aspects of the text. Word level embeddings such as Glove, FastText, Emoji along with sentence level embeddings like Skip-Thought, DeepMoji {\&} Unsupervised Sentiment Neuron were used as input features to our architecture. We democratize the learning using ensembling of models with different parameters to produce the final output. This paper discusses comparative analysis of the significance of these embeddings and our approach for the task.
E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text
Authors: Harsh Patel
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: BIG-bench Machine Learning

This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is {''}Contextual Emotion Detection in Texts{''}. The paper includes the explanation of an architecture that I created by exploiting the embedding layers of Word2Vec and GloVe using LSTMs as memory unit cells which detects approximate emotion of chats between two people in the English language provided in the textual form. The set of emotions on which the model was trained was Happy, Sad, Angry and Others. The paper also includes an analysis of different conventional machine learning algorithms in comparison to E-LSTM.
ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection
Authors: Jos{\'e}-{\'A}ngel Gonz{\'a}lez, Llu{\'\i}s-F. Hurtado, Ferran Pla
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This paper describes the approach developed by the ELiRF-UPV team at SemEval 2019 Task 3: Contextual Emotion Detection in Text. We have developed a Snapshot Ensemble of 1D Hierarchical Convolutional Neural Networks to extract features from 3-turn conversations in order to perform contextual emotion detection in text. This Snapshot Ensemble is obtained by averaging the models selected by a Genetic Algorithm that optimizes the evaluation measure. The proposed ensemble obtains better results than a single model and it obtains competitive and promising results on Contextual Emotion Detection in Text.
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning
Authors: Hani Al-Omari, Malak Abdullah, Nabeel Bassam
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Word Embeddings

Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).
EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text
Authors: Nikhil Chakravartula, Vijayasaradhi Indurthi
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Word Embeddings

This paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context of a text conversation. Our system is a stacked Bidirectional LSTM, equipped with attention on top of word embeddings pre-trained on a large collection of Twitter data. In this paper, apart from describing our official submission, we elucidate how different deep learning models respond to this task.
EmoSense at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations
Authors: Sergey Smetanin
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Word Embeddings

In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 {``}EmoContext{''}. We designed a specific architecture of bidirectional LSTM which allows not only to learn semantic and sentiment feature representation, but also to capture user-specific conversation features. To fine-tune word embeddings using distant supervision we additionally collected a significant amount of emotional texts. The system achieved 72.59{\%} micro-average F1 score for emotion classes on the test dataset, thereby significantly outperforming the officially-released baseline. Word embeddings and the source code were released for the research community.
EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination
Authors: Abdessalam Bouchekif, Praveen Joshi, Latifa Bouchekif, Haithem Afli
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Transfer Learning

Messaging platforms like WhatsApp, Facebook Messenger and Twitter have gained recently much popularity owing to their ability in connecting users in real-time. The content of these textual messages can be a useful resource for text mining to discover and unhide various aspects, including emotions. In this paper we present our submission for SemEval 2019 task {`}EmoContext{'}. The task consists of classifying a given textual dialogue into one of four emotion classes: Angry, Happy, Sad and Others. Our proposed system is based on the combination of different deep neural networks techniques. In particular, we use Recurrent Neural Networks (LSTM, B-LSTM, GRU, B-GRU), Convolutional Neural Network (CNN) and Transfer Learning (TL) methodes. Our final system, achieves an F1 score of 74.51{\%} on the subtask evaluation dataset.